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fix transformation description printing.
author Frederic Bastien <nouiz@nouiz.org>
date Mon, 22 Nov 2010 15:13:45 -0500
parents 51213beaed8b
children bd7d11089a47
rev   line source
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1 \documentclass{article} % For LaTeX2e
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2 \usepackage{nips10submit_e,times}
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3 \usepackage{wrapfig}
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4 \usepackage{amsthm,amsmath,bbm}
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5 \usepackage[psamsfonts]{amssymb}
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6 \usepackage{algorithm,algorithmic}
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7 \usepackage[utf8]{inputenc}
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8 \usepackage{graphicx,subfigure}
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9 \usepackage[numbers]{natbib}
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10
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11 \addtolength{\textwidth}{20mm}
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12 \addtolength{\textheight}{20mm}
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13 \addtolength{\topmargin}{-10mm}
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14 \addtolength{\evensidemargin}{-10mm}
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15 \addtolength{\oddsidemargin}{-10mm}
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16
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17 %\setlength\parindent{0mm}
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18
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19 \title{Deep Self-Taught Learning for Handwritten Character Recognition}
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20 \author{
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21 Frédéric Bastien,
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22 Yoshua Bengio,
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23 Arnaud Bergeron,
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24 Nicolas Boulanger-Lewandowski,
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25 Thomas Breuel,\\
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26 {\bf Youssouf Chherawala,
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27 Moustapha Cisse,
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28 Myriam Côté,
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29 Dumitru Erhan,
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30 Jeremy Eustache,}\\
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31 {\bf Xavier Glorot,
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32 Xavier Muller,
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33 Sylvain Pannetier Lebeuf,
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34 Razvan Pascanu,} \\
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35 {\bf Salah Rifai,
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36 Francois Savard,
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37 Guillaume Sicard}\\
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38 Dept. IRO, U. Montreal
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39 }
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40
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41 \begin{document}
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42
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43 %\makeanontitle
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44 \maketitle
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45
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46 \vspace*{-2mm}
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47 \begin{abstract}
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48 Recent theoretical and empirical work in statistical machine learning has
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49 demonstrated the importance of learning algorithms for deep
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50 architectures, i.e., function classes obtained by composing multiple
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51 non-linear transformations. Self-taught learning (exploiting unlabeled
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52 examples or examples from other distributions) has already been applied
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53 to deep learners, but mostly to show the advantage of unlabeled
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54 examples. Here we explore the advantage brought by {\em out-of-distribution examples}.
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55 For this purpose we
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56 developed a powerful generator of stochastic variations and noise
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57 processes for character images, including not only affine transformations
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58 but also slant, local elastic deformations, changes in thickness,
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59 background images, grey level changes, contrast, occlusion, and various
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60 types of noise. The out-of-distribution examples are obtained from these
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61 highly distorted images or by including examples of object classes
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62 different from those in the target test set.
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63 We show that {\em deep learners benefit
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64 more from them than a corresponding shallow learner}, at least in the area of
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65 handwritten character recognition. In fact, we show that they reach
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66 human-level performance on both handwritten digit classification and
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67 62-class handwritten character recognition.
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68 \end{abstract}
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69 \vspace*{-3mm}
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70
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71 \section{Introduction}
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72 \vspace*{-1mm}
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73
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74 {\bf Deep Learning} has emerged as a promising new area of research in
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75 statistical machine learning~\citep{Hinton06,ranzato-07-small,Bengio-nips-2006,VincentPLarochelleH2008-very-small,ranzato-08,TaylorHintonICML2009,Larochelle-jmlr-2009,Salakhutdinov+Hinton-2009,HonglakL2009,HonglakLNIPS2009,Jarrett-ICCV2009,Taylor-cvpr-2010}. See \citet{Bengio-2009} for a review.
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76 Learning algorithms for deep architectures are centered on the learning
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77 of useful representations of data, which are better suited to the task at hand,
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78 and are organized in a hierarchy with multiple levels.
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79 This is in part inspired by observations of the mammalian visual cortex,
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80 which consists of a chain of processing elements, each of which is associated with a
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81 different representation of the raw visual input. In fact,
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82 it was found recently that the features learnt in deep architectures resemble
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83 those observed in the first two of these stages (in areas V1 and V2
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84 of visual cortex)~\citep{HonglakL2008}, and that they become more and
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85 more invariant to factors of variation (such as camera movement) in
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86 higher layers~\citep{Goodfellow2009}.
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87 It has been hypothesized that learning a hierarchy of features increases the
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88 ease and practicality of developing representations that are at once
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89 tailored to specific tasks, yet are able to borrow statistical strength
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90 from other related tasks (e.g., modeling different kinds of objects). Finally, learning the
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91 feature representation can lead to higher-level (more abstract, more
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92 general) features that are more robust to unanticipated sources of
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93 variance extant in real data.
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94
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95 {\bf Self-taught learning}~\citep{RainaR2007} is a paradigm that combines principles
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96 of semi-supervised and multi-task learning: the learner can exploit examples
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97 that are unlabeled and possibly come from a distribution different from the target
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98 distribution, e.g., from other classes than those of interest.
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99 It has already been shown that deep learners can clearly take advantage of
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100 unsupervised learning and unlabeled examples~\citep{Bengio-2009,WestonJ2008-small},
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101 but more needs to be done to explore the impact
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102 of {\em out-of-distribution} examples and of the multi-task setting
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103 (one exception is~\citep{CollobertR2008}, which uses a different kind
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104 of learning algorithm). In particular the {\em relative
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105 advantage} of deep learning for these settings has not been evaluated.
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106 The hypothesis discussed in the conclusion is that a deep hierarchy of features
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107 may be better able to provide sharing of statistical strength
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108 between different regions in input space or different tasks.
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109
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110 \iffalse
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111 Whereas a deep architecture can in principle be more powerful than a
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112 shallow one in terms of representation, depth appears to render the
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113 training problem more difficult in terms of optimization and local minima.
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114 It is also only recently that successful algorithms were proposed to
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115 overcome some of these difficulties. All are based on unsupervised
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116 learning, often in an greedy layer-wise ``unsupervised pre-training''
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117 stage~\citep{Bengio-2009}.
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118 The principle is that each layer starting from
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119 the bottom is trained to represent its input (the output of the previous
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120 layer). After this
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121 unsupervised initialization, the stack of layers can be
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122 converted into a deep supervised feedforward neural network and fine-tuned by
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123 stochastic gradient descent.
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124 One of these layer initialization techniques,
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125 applied here, is the Denoising
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126 Auto-encoder~(DA)~\citep{VincentPLarochelleH2008-very-small} (see
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127 Figure~\ref{fig:da}), which performed similarly or
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128 better~\citep{VincentPLarochelleH2008-very-small} than previously
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129 proposed Restricted Boltzmann Machines (RBM)~\citep{Hinton06}
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130 in terms of unsupervised extraction
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131 of a hierarchy of features useful for classification. Each layer is trained
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132 to denoise its input, creating a layer of features that can be used as
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133 input for the next layer, forming a Stacked Denoising Auto-encoder (SDA).
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134 Note that training a Denoising Auto-encoder
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135 can actually been seen as training a particular RBM by an inductive
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136 principle different from maximum likelihood~\citep{Vincent-SM-2010},
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137 namely by Score Matching~\citep{Hyvarinen-2005,HyvarinenA2008}.
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138 \fi
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139
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140 Previous comparative experimental results with stacking of RBMs and DAs
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141 to build deep supervised predictors had shown that they could outperform
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142 shallow architectures in a variety of settings, especially
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143 when the data involves complex interactions between many factors of
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144 variation~\citep{LarochelleH2007,Bengio-2009}. Other experiments have suggested
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145 that the unsupervised layer-wise pre-training acted as a useful
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146 prior~\citep{Erhan+al-2010} that allows one to initialize a deep
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147 neural network in a relatively much smaller region of parameter space,
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148 corresponding to better generalization.
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149
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150 To further the understanding of the reasons for the good performance
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151 observed with deep learners, we focus here on the following {\em hypothesis}:
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152 intermediate levels of representation, especially when there are
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153 more such levels, can be exploited to {\bf share
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154 statistical strength across different but related types of examples},
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155 such as examples coming from other tasks than the task of interest
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156 (the multi-task setting), or examples coming from an overlapping
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157 but different distribution (images with different kinds of perturbations
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158 and noises, here). This is consistent with the hypotheses discussed
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159 in~\citet{Bengio-2009} regarding the potential advantage
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160 of deep learning and the idea that more levels of representation can
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161 give rise to more abstract, more general features of the raw input.
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162
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163 This hypothesis is related to a learning setting called
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164 {\bf self-taught learning}~\citep{RainaR2007}, which combines principles
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165 of semi-supervised and multi-task learning: the learner can exploit examples
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166 that are unlabeled and possibly come from a distribution different from the target
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167 distribution, e.g., from other classes than those of interest.
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168 It has already been shown that deep learners can clearly take advantage of
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169 unsupervised learning and unlabeled examples~\citep{Bengio-2009,WestonJ2008-small},
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170 but more needed to be done to explore the impact
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171 of {\em out-of-distribution} examples and of the {\em multi-task} setting
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172 (one exception is~\citep{CollobertR2008}, which shares and uses unsupervised
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173 pre-training only with the first layer). In particular the {\em relative
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174 advantage of deep learning} for these settings has not been evaluated.
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175
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176
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177 %
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178 The {\bf main claim} of this paper is that deep learners (with several levels of representation) can
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179 {\bf benefit more from out-of-distribution examples than shallow learners} (with a single
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180 level), both in the context of the multi-task setting and from
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181 perturbed examples. Because we are able to improve on state-of-the-art
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182 performance and reach human-level performance
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183 on a large-scale task, we consider that this paper is also a contribution
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184 to advance the application of machine learning to handwritten character recognition.
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185 More precisely, we ask and answer the following questions:
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186
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187 %\begin{enumerate}
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188 $\bullet$ %\item
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189 Do the good results previously obtained with deep architectures on the
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190 MNIST digit images generalize to the setting of a similar but much larger and richer
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191 dataset, the NIST special database 19, with 62 classes and around 800k examples?
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192
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193 $\bullet$ %\item
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194 To what extent does the perturbation of input images (e.g. adding
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195 noise, affine transformations, background images) make the resulting
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196 classifiers better not only on similarly perturbed images but also on
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197 the {\em original clean examples}? We study this question in the
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198 context of the 62-class and 10-class tasks of the NIST special database 19.
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199
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200 $\bullet$ %\item
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201 Do deep architectures {\em benefit {\bf more} from such out-of-distribution}
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202 examples, in particular do they benefit more from
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203 examples that are perturbed versions of the examples from the task of interest?
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204
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205 $\bullet$ %\item
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206 Similarly, does the feature learning step in deep learning algorithms benefit {\bf more}
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207 from training with moderately {\em different classes} (i.e. a multi-task learning scenario) than
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208 a corresponding shallow and purely supervised architecture?
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209 We train on 62 classes and test on 10 (digits) or 26 (upper case or lower case)
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210 to answer this question.
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211 %\end{enumerate}
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212
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213 Our experimental results provide positive evidence towards all of these questions,
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214 as well as {\bf classifiers that reach human-level performance on 62-class isolated character
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215 recognition and beat previously published results on the NIST dataset (special database 19)}.
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216 To achieve these results, we introduce in the next section a sophisticated system
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217 for stochastically transforming character images and then explain the methodology,
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218 which is based on training with or without these transformed images and testing on
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219 clean ones.
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220 Code for generating these transformations as well as for the deep learning
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221 algorithms are made available at {\tt http://hg.assembla.com/ift6266}.
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222
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223 \vspace*{-3mm}
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224 %\newpage
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225 \section{Perturbed and Transformed Character Images}
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226 \label{s:perturbations}
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227 \vspace*{-2mm}
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228
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229 \begin{minipage}[h]{\linewidth}
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230 \begin{wrapfigure}[8]{l}{0.15\textwidth}
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231 %\begin{minipage}[b]{0.14\linewidth}
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232 \vspace*{-5mm}
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233 \begin{center}
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234 \includegraphics[scale=.4]{images/Original.png}\\
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235 {\bf Original}
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236 \end{center}
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237 \end{wrapfigure}
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238 %\vspace{0.7cm}
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239 %\end{minipage}%
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240 %\hspace{0.3cm}\begin{minipage}[b]{0.86\linewidth}
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241 This section describes the different transformations we used to stochastically
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242 transform $32 \times 32$ source images (such as the one on the left)
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243 in order to obtain data from a larger distribution which
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244 covers a domain substantially larger than the clean characters distribution from
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245 which we start.
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246 Although character transformations have been used before to
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247 improve character recognizers, this effort is on a large scale both
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248 in number of classes and in the complexity of the transformations, hence
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249 in the complexity of the learning task.
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250 More details can
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251 be found in this technical report~\citep{ARXIV-2010}.
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252 The code for these transformations (mostly python) is available at
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253 {\tt http://hg.assembla.com/ift6266}. All the modules in the pipeline share
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254 a global control parameter ($0 \le complexity \le 1$) that allows one to modulate the
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255 amount of deformation or noise introduced.
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256 There are two main parts in the pipeline. The first one,
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257 from thickness to pinch, performs transformations. The second
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258 part, from blur to contrast, adds different kinds of noise.
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259 \end{minipage}
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260
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261 \vspace*{1mm}
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262 %\subsection{Transformations}
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263 {\large\bf 2.1 Transformations}
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264 \vspace*{1mm}
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265
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266
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267 \begin{minipage}[h]{\linewidth}
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268 \begin{wrapfigure}[7]{l}{0.15\textwidth}
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269 %\begin{minipage}[b]{0.14\linewidth}
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270 %\centering
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271 \begin{center}
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272 \vspace*{-5mm}
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273 \includegraphics[scale=.4]{images/Thick_only.png}\\
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274 {\bf Thickness}
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275 \end{center}
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276 %\vspace{.6cm}
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277 %\end{minipage}%
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278 %\hspace{0.3cm}\begin{minipage}[b]{0.86\linewidth}
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279 \end{wrapfigure}
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280 To change character {\bf thickness}, morphological operators of dilation and erosion~\citep{Haralick87,Serra82}
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281 are applied. The neighborhood of each pixel is multiplied
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282 element-wise with a {\em structuring element} matrix.
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283 The pixel value is replaced by the maximum or the minimum of the resulting
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284 matrix, respectively for dilation or erosion. Ten different structural elements with
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285 increasing dimensions (largest is $5\times5$) were used. For each image,
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286 randomly sample the operator type (dilation or erosion) with equal probability and one structural
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287 element from a subset of the $n=round(m \times complexity)$ smallest structuring elements
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288 where $m=10$ for dilation and $m=6$ for erosion (to avoid completely erasing thin characters).
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289 A neutral element (no transformation)
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290 is always present in the set.
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291 %\vspace{.4cm}
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292 \end{minipage}
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293 \vspace*{3mm}
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294
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295 \begin{minipage}[h]{\linewidth}
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296 \begin{wrapfigure}[7]{l}{0.15\textwidth}
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297 %\begin{minipage}[b]{0.14\linewidth}
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298 %\centering
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299 \begin{center}
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300 \vspace*{-5mm}
604
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301 \includegraphics[scale=.4]{images/Slant_only.png}\\
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302 {\bf Slant}
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303 \end{center}
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304 \end{wrapfigure}
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305
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306 %\end{minipage}%
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307 %\hspace{0.3cm}
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308 %\begin{minipage}[b]{0.83\linewidth}
604
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309 %\centering
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310 To produce {\bf slant}, each row of the image is shifted
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311 proportionally to its height: $shift = round(slant \times height)$.
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312 $slant \sim U[-complexity,complexity]$.
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313 The shift is randomly chosen to be either to the left or to the right.
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314 %\vspace{8mm}
604
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315 \end{minipage}
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316 \vspace*{10mm}
604
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317
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318 \begin{minipage}[h]{\linewidth}
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319 %\begin{minipage}[b]{0.14\linewidth}
604
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320 %\centering
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321 \begin{wrapfigure}[7]{l}{0.15\textwidth}
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322 \begin{center}
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323 \vspace*{-5mm}
604
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324 \includegraphics[scale=.4]{images/Affine_only.png}\\
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325 {\small {\bf Affine \mbox{Transformation}}}
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326 \end{center}
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327 \end{wrapfigure}
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328 %\end{minipage}%
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329 %\hspace{0.3cm}\begin{minipage}[b]{0.86\linewidth}
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330 A $2 \times 3$ {\bf affine transform} matrix (with
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331 parameters $(a,b,c,d,e,f)$) is sampled according to the $complexity$.
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332 Output pixel $(x,y)$ takes the value of input pixel
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333 nearest to $(ax+by+c,dx+ey+f)$,
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334 producing scaling, translation, rotation and shearing.
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335 Marginal distributions of $(a,b,c,d,e,f)$ have been tuned to
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336 forbid large rotations (to avoid confusing classes) but to give good
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337 variability of the transformation: $a$ and $d$ $\sim U[1-3
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338 complexity,1+3\,complexity]$, $b$ and $e$ $\sim U[-3 \,complexity,3\,
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339 complexity]$, and $c$ and $f \sim U[-4 \,complexity, 4 \,
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340 complexity]$.\\
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341 %\end{minipage}
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342 \end{minipage}
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343 \vspace*{3mm}
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344
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345 \iffalse
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346 \vspace*{-4.5mm}
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347
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348 \begin{minipage}[h]{\linewidth}
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349 \begin{wrapfigure}[7]{l}{0.15\textwidth}
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350 %\hspace*{-8mm}\begin{minipage}[b]{0.25\linewidth}
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351 %\centering
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352 \begin{center}
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353 \vspace*{-4mm}
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354 \includegraphics[scale=.4]{images/Localelasticdistorsions_only.png}\\
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355 {\bf Local Elastic Deformation}
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356 \end{center}
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357 \end{wrapfigure}
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358 %\end{minipage}%
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359 %\hspace{-3mm}\begin{minipage}[b]{0.85\linewidth}
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360 %\vspace*{-20mm}
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361 The {\bf local elastic deformation}
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362 module induces a ``wiggly'' effect in the image, following~\citet{SimardSP03-short},
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363 which provides more details.
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364 The intensity of the displacement fields is given by
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365 $\alpha = \sqrt[3]{complexity} \times 10.0$, which are
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366 convolved with a Gaussian 2D kernel (resulting in a blur) of
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367 standard deviation $\sigma = 10 - 7 \times\sqrt[3]{complexity}$.
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368 %\vspace{.9cm}
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369 \end{minipage}
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370
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371 \vspace*{7mm}
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372
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373 %\begin{minipage}[b]{0.14\linewidth}
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374 %\centering
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375 \begin{minipage}[h]{\linewidth}
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376 \begin{wrapfigure}[7]{l}{0.15\textwidth}
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377 \vspace*{-5mm}
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378 \begin{center}
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379 \includegraphics[scale=.4]{images/Pinch_only.png}\\
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380 {\bf Pinch}
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381 \end{center}
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382 \end{wrapfigure}
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383 %\vspace{.6cm}
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384 %\end{minipage}%
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385 %\hspace{0.3cm}\begin{minipage}[b]{0.86\linewidth}
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386 The {\bf pinch} module applies the ``Whirl and pinch'' GIMP filter with whirl set to 0.
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387 A pinch is ``similar to projecting the image onto an elastic
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388 surface and pressing or pulling on the center of the surface'' (GIMP documentation manual).
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389 For a square input image, draw a radius-$r$ disk
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390 around its center $C$. Any pixel $P$ belonging to
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391 that disk has its value replaced by
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392 the value of a ``source'' pixel in the original image,
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393 on the line that goes through $C$ and $P$, but
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394 at some other distance $d_2$. Define $d_1=distance(P,C)$
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395 and $d_2 = sin(\frac{\pi{}d_1}{2r})^{-pinch} \times
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396 d_1$, where $pinch$ is a parameter of the filter.
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397 The actual value is given by bilinear interpolation considering the pixels
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398 around the (non-integer) source position thus found.
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399 Here $pinch \sim U[-complexity, 0.7 \times complexity]$.
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400 %\vspace{1.5cm}
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401 \end{minipage}
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402
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403 \vspace{1mm}
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404
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405 {\large\bf 2.2 Injecting Noise}
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406 %\subsection{Injecting Noise}
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407 \vspace{2mm}
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408
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409 \begin{minipage}[h]{\linewidth}
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410 %\vspace*{-.2cm}
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411 \begin{minipage}[t]{0.14\linewidth}
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412 \centering
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413 \vspace*{-2mm}
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414 \includegraphics[scale=.4]{images/Motionblur_only.png}\\
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415 {\bf Motion Blur}
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416 \end{minipage}%
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417 \hspace{0.3cm}\begin{minipage}[t]{0.83\linewidth}
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418 %\vspace*{.5mm}
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419 The {\bf motion blur} module is GIMP's ``linear motion blur'', which
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420 has parameters $length$ and $angle$. The value of
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421 a pixel in the final image is approximately the mean of the first $length$ pixels
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422 found by moving in the $angle$ direction,
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423 $angle \sim U[0,360]$ degrees, and $length \sim {\rm Normal}(0,(3 \times complexity)^2)$.
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424 \vspace{5mm}
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425 \end{minipage}
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426 \end{minipage}
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427
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428 \vspace*{1mm}
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429
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430 \begin{minipage}[h]{\linewidth}
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431 \begin{minipage}[t]{0.14\linewidth}
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432 \centering
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433 \includegraphics[scale=.4]{images/occlusion_only.png}\\
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434 {\bf Occlusion}
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435 %\vspace{.5cm}
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436 \end{minipage}%
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437 \hspace{0.3cm}\begin{minipage}[t]{0.83\linewidth}
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438 \vspace*{-18mm}
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439 The {\bf occlusion} module selects a random rectangle from an {\em occluder} character
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440 image and places it over the original {\em occluded}
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441 image. Pixels are combined by taking the max(occluder, occluded),
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442 i.e. keeping the lighter ones.
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443 The rectangle corners
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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444 are sampled so that larger complexity gives larger rectangles.
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445 The destination position in the occluded image are also sampled
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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446 according to a normal distribution (more details in~\citet{ift6266-tr-anonymous}).
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447 This module is skipped with probability 60\%.
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448 %\vspace{7mm}
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449 \end{minipage}
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450 \end{minipage}
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451
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452 \vspace*{1mm}
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453
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454 \begin{wrapfigure}[8]{l}{0.15\textwidth}
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455 \vspace*{-6mm}
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456 \begin{center}
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457 %\begin{minipage}[t]{0.14\linewidth}
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458 %\centering
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459 \includegraphics[scale=.4]{images/Bruitgauss_only.png}\\
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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460 {\bf Gaussian Smoothing}
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461 \end{center}
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462 \end{wrapfigure}
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463 %\vspace{.5cm}
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464 %\end{minipage}%
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465 %\hspace{0.3cm}\begin{minipage}[t]{0.86\linewidth}
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466 With the {\bf Gaussian smoothing} module,
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467 different regions of the image are spatially smoothed.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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468 This is achieved by first convolving
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469 the image with an isotropic Gaussian kernel of
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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diff changeset
470 size and variance chosen uniformly in the ranges $[12,12 + 20 \times
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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diff changeset
471 complexity]$ and $[2,2 + 6 \times complexity]$. This filtered image is normalized
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472 between $0$ and $1$. We also create an isotropic weighted averaging window, of the
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diff changeset
473 kernel size, with maximum value at the center. For each image we sample
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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diff changeset
474 uniformly from $3$ to $3 + 10 \times complexity$ pixels that will be
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diff changeset
475 averaging centers between the original image and the filtered one. We
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476 initialize to zero a mask matrix of the image size. For each selected pixel
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477 we add to the mask the averaging window centered on it. The final image is
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478 computed from the following element-wise operation: $\frac{image + filtered\_image
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diff changeset
479 \times mask}{mask+1}$.
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480 This module is skipped with probability 75\%.
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481 %\end{minipage}
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482
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483 \newpage
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484
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485 \vspace*{-9mm}
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486
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487 %\hspace*{-3mm}\begin{minipage}[t]{0.18\linewidth}
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488 %\centering
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489 \begin{minipage}[t]{\linewidth}
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490 \begin{wrapfigure}[7]{l}{0.15\textwidth}
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491 \vspace*{-5mm}
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492 \begin{center}
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493 \includegraphics[scale=.4]{images/Permutpixel_only.png}\\
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494 {\small\bf Permute Pixels}
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495 \end{center}
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496 \end{wrapfigure}
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497 %\end{minipage}%
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498 %\hspace{-0cm}\begin{minipage}[t]{0.86\linewidth}
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499 %\vspace*{-20mm}
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500 This module {\bf permutes neighbouring pixels}. It first selects a
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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diff changeset
501 fraction $\frac{complexity}{3}$ of pixels randomly in the image. Each
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502 of these pixels is then sequentially exchanged with a random pixel
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diff changeset
503 among its four nearest neighbors (on its left, right, top or bottom).
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504 This module is skipped with probability 80\%.\\
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505 \vspace*{1mm}
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506 \end{minipage}
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507
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508 \vspace{-3mm}
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509
51213beaed8b draft of NIPS 2010 workshop camera-ready version
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510 \begin{minipage}[t]{\linewidth}
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511 \begin{wrapfigure}[7]{l}{0.15\textwidth}
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512 %\vspace*{-3mm}
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513 \begin{center}
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514 %\hspace*{-3mm}\begin{minipage}[t]{0.18\linewidth}
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515 %\centering
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516 \vspace*{-5mm}
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517 \includegraphics[scale=.4]{images/Distorsiongauss_only.png}\\
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518 {\small \bf Gauss. Noise}
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519 \end{center}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
520 \end{wrapfigure}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
521 %\end{minipage}%
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
522 %\hspace{0.3cm}\begin{minipage}[t]{0.86\linewidth}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
523 \vspace*{12mm}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
524 The {\bf Gaussian noise} module simply adds, to each pixel of the image independently, a
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
525 noise $\sim Normal(0,(\frac{complexity}{10})^2)$.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
526 This module is skipped with probability 70\%.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
527 %\vspace{1.1cm}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
528 \end{minipage}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
529
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
530 \vspace*{1.2cm}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
531
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
532 \begin{minipage}[t]{\linewidth}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
533 \begin{minipage}[t]{0.14\linewidth}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
534 \centering
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
535 \includegraphics[scale=.4]{images/background_other_only.png}\\
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
536 {\small \bf Bg Image}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
537 \end{minipage}%
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
538 \hspace{0.3cm}\begin{minipage}[t]{0.83\linewidth}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
539 \vspace*{-18mm}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
540 Following~\citet{Larochelle-jmlr-2009}, the {\bf background image} module adds a random
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
541 background image behind the letter, from a randomly chosen natural image,
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
542 with contrast adjustments depending on $complexity$, to preserve
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
543 more or less of the original character image.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
544 %\vspace{.8cm}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
545 \end{minipage}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
546 \end{minipage}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
547 %\vspace{-.7cm}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
548
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
549 \begin{minipage}[t]{0.14\linewidth}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
550 \centering
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
551 \includegraphics[scale=.4]{images/Poivresel_only.png}\\
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
552 {\small \bf Salt \& Pepper}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
553 \end{minipage}%
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
554 \hspace{0.3cm}\begin{minipage}[t]{0.83\linewidth}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
555 \vspace*{-18mm}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
556 The {\bf salt and pepper noise} module adds noise $\sim U[0,1]$ to random subsets of pixels.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
557 The number of selected pixels is $0.2 \times complexity$.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
558 This module is skipped with probability 75\%.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
559 %\vspace{.9cm}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
560 \end{minipage}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
561 %\vspace{-.7cm}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
562
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
563 \vspace{1mm}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
564
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
565 \begin{minipage}[t]{\linewidth}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
566 \begin{wrapfigure}[7]{l}{0.14\textwidth}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
567 %\begin{minipage}[t]{0.14\linewidth}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
568 %\centering
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
569 \begin{center}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
570 \vspace*{-4mm}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
571 \hspace*{-1mm}\includegraphics[scale=.4]{images/Rature_only.png}\\
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
572 {\bf Scratches}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
573 %\end{minipage}%
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
574 \end{center}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
575 \end{wrapfigure}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
576 %\hspace{0.3cm}\begin{minipage}[t]{0.86\linewidth}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
577 %\vspace{.4cm}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
578 The {\bf scratches} module places line-like white patches on the image. The
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
579 lines are heavily transformed images of the digit ``1'' (one), chosen
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
580 at random among 500 such 1 images,
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
581 randomly cropped and rotated by an angle $\sim Normal(0,(100 \times
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
582 complexity)^2$ (in degrees), using bi-cubic interpolation.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
583 Two passes of a grey-scale morphological erosion filter
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
584 are applied, reducing the width of the line
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
585 by an amount controlled by $complexity$.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
586 This module is skipped with probability 85\%. The probabilities
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
587 of applying 1, 2, or 3 patches are (50\%,30\%,20\%).
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
588 \end{minipage}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
589
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
590 \vspace*{1mm}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
591
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
592 \begin{minipage}[t]{0.25\linewidth}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
593 \centering
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
594 \hspace*{-16mm}\includegraphics[scale=.4]{images/Contrast_only.png}\\
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
595 {\bf Grey Level \& Contrast}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
596 \end{minipage}%
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
597 \hspace{-12mm}\begin{minipage}[t]{0.82\linewidth}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
598 \vspace*{-18mm}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
599 The {\bf grey level and contrast} module changes the contrast by changing grey levels, and may invert the image polarity (white
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
600 to black and black to white). The contrast is $C \sim U[1-0.85 \times complexity,1]$
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
601 so the image is normalized into $[\frac{1-C}{2},1-\frac{1-C}{2}]$. The
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
602 polarity is inverted with probability 50\%.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
603 %\vspace{.7cm}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
604 \end{minipage}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
605 \vspace{2mm}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
606
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
607 \fi
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
608
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
609 \iffalse
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
610 \begin{figure}[ht]
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
611 \centerline{\resizebox{.9\textwidth}{!}{\includegraphics{images/example_t.png}}}\\
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
612 \caption{Illustration of the pipeline of stochastic
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
613 transformations applied to the image of a lower-case \emph{t}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
614 (the upper left image). Each image in the pipeline (going from
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
615 left to right, first top line, then bottom line) shows the result
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
616 of applying one of the modules in the pipeline. The last image
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
617 (bottom right) is used as training example.}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
618 \label{fig:pipeline}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
619 \end{figure}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
620 \fi
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
621
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
622 \vspace*{-3mm}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
623 \section{Experimental Setup}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
624 \vspace*{-1mm}
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parents:
diff changeset
625
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
626 Much previous work on deep learning had been performed on
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
627 the MNIST digits task~\citep{Hinton06,ranzato-07-small,Bengio-nips-2006,Salakhutdinov+Hinton-2009},
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
628 with 60~000 examples, and variants involving 10~000
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
629 examples~\citep{Larochelle-jmlr-toappear-2008,VincentPLarochelleH2008}.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
630 The focus here is on much larger training sets, from 10 times to
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
631 to 1000 times larger, and 62 classes.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
632
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
633 The first step in constructing the larger datasets (called NISTP and P07) is to sample from
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
634 a {\em data source}: {\bf NIST} (NIST database 19), {\bf Fonts}, {\bf Captchas},
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
635 and {\bf OCR data} (scanned machine printed characters). Once a character
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
636 is sampled from one of these {\em data sources} (chosen randomly), the second step is to
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
637 apply a pipeline of transformations and/or noise processes described in section \ref{s:perturbations}.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
638
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
639 To provide a baseline of error rate comparison we also estimate human performance
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
640 on both the 62-class task and the 10-class digits task.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
641 We compare the best Multi-Layer Perceptrons (MLP) against
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
642 the best Stacked Denoising Auto-encoders (SDA), when
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
643 both models' hyper-parameters are selected to minimize the validation set error.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
644 We also provide a comparison against a precise estimate
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
645 of human performance obtained via Amazon's Mechanical Turk (AMT)
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
646 service ({\tt http://mturk.com}).
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
647 AMT users are paid small amounts
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
648 of money to perform tasks for which human intelligence is required.
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parents:
diff changeset
649 An incentive for them to do the job right is that payment can be denied
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
650 if the job is not properly done.
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diff changeset
651 Mechanical Turk has been used extensively in natural language processing and vision.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
652 %processing \citep{SnowEtAl2008} and vision
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parents:
diff changeset
653 %\citep{SorokinAndForsyth2008,whitehill09}.
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parents:
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654 AMT users were presented
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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diff changeset
655 with 10 character images at a time (from a test set) and asked to choose 10 corresponding ASCII
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
656 characters. They were forced to choose a single character class (either among the
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
657 62 or 10 character classes) for each image.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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diff changeset
658 80 subjects classified 2500 images per (dataset,task) pair.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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diff changeset
659 Different humans labelers sometimes provided a different label for the same
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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660 example, and we were able to estimate the error variance due to this effect
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
661 because each image was classified by 3 different persons.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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diff changeset
662 The average error of humans on the 62-class task NIST test set
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
663 is 18.2\%, with a standard error of 0.1\%.
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diff changeset
664
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665 \vspace*{-3mm}
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666 \subsection{Data Sources}
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diff changeset
667 \vspace*{-2mm}
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668
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diff changeset
669 %\begin{itemize}
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diff changeset
670 %\item
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parents:
diff changeset
671 {\bf NIST.}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
672 Our main source of characters is the NIST Special Database 19~\citep{Grother-1995},
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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diff changeset
673 widely used for training and testing character
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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diff changeset
674 recognition systems~\citep{Granger+al-2007,Cortes+al-2000,Oliveira+al-2002-short,Milgram+al-2005}.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
675 The dataset is composed of 814255 digits and characters (upper and lower cases), with hand checked classifications,
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
676 extracted from handwritten sample forms of 3600 writers. The characters are labelled by one of the 62 classes
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
677 corresponding to ``0''-``9'',``A''-``Z'' and ``a''-``z''. The dataset contains 8 parts (partitions) of varying complexity.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
678 The fourth partition (called $hsf_4$, 82587 examples),
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
679 experimentally recognized to be the most difficult one, is the one recommended
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
680 by NIST as a testing set and is used in our work as well as some previous work~\citep{Granger+al-2007,Cortes+al-2000,Oliveira+al-2002-short,Milgram+al-2005}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
681 for that purpose. We randomly split the remainder (731,668 examples) into a training set and a validation set for
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
682 model selection.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
683 The performances reported by previous work on that dataset mostly use only the digits.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
684 Here we use all the classes both in the training and testing phase. This is especially
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
685 useful to estimate the effect of a multi-task setting.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
686 The distribution of the classes in the NIST training and test sets differs
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
687 substantially, with relatively many more digits in the test set, and a more uniform distribution
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
688 of letters in the test set (whereas in the training set they are distributed
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
689 more like in natural text).
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diff changeset
690 \vspace*{-1mm}
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diff changeset
691
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diff changeset
692 %\item
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
693 {\bf Fonts.}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
694 In order to have a good variety of sources we downloaded an important number of free fonts from:
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
695 {\tt http://cg.scs.carleton.ca/\textasciitilde luc/freefonts.html}.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
696 % TODO: pointless to anonymize, it's not pointing to our work
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
697 Including an operating system's (Windows 7) fonts, there is a total of $9817$ different fonts that we can choose uniformly from.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
698 The chosen {\tt ttf} file is either used as input of the Captcha generator (see next item) or, by producing a corresponding image,
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
699 directly as input to our models.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
700 \vspace*{-1mm}
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parents:
diff changeset
701
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parents:
diff changeset
702 %\item
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
703 {\bf Captchas.}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
704 The Captcha data source is an adaptation of the \emph{pycaptcha} library (a Python-based captcha generator library) for
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
705 generating characters of the same format as the NIST dataset. This software is based on
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
706 a random character class generator and various kinds of transformations similar to those described in the previous sections.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
707 In order to increase the variability of the data generated, many different fonts are used for generating the characters.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
708 Transformations (slant, distortions, rotation, translation) are applied to each randomly generated character with a complexity
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
709 depending on the value of the complexity parameter provided by the user of the data source.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
710 %Two levels of complexity are allowed and can be controlled via an easy to use facade class. %TODO: what's a facade class?
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
711 \vspace*{-1mm}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
712
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
713 %\item
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
714 {\bf OCR data.}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
715 A large set (2 million) of scanned, OCRed and manually verified machine-printed
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
716 characters where included as an
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
717 additional source. This set is part of a larger corpus being collected by the Image Understanding
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
718 Pattern Recognition Research group led by Thomas Breuel at University of Kaiserslautern
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
719 ({\tt http://www.iupr.com}), and which will be publicly released.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
720 %TODO: let's hope that Thomas is not a reviewer! :) Seriously though, maybe we should anonymize this
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
721 %\end{itemize}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
722
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
723 \vspace*{-3mm}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
724 \subsection{Data Sets}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
725 \vspace*{-2mm}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
726
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
727 All data sets contain 32$\times$32 grey-level images (values in $[0,1]$) associated with a label
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
728 from one of the 62 character classes. They are obtained from the optional application of the
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
729 perturbation pipeline to iid samples from the datasources, and they are randomly split into
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
730 training set, validation set, and test set.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
731 %\begin{itemize}
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parents:
diff changeset
732 \vspace*{-1mm}
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parents:
diff changeset
733
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
734 %\item
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
735 {\bf NIST.} This is the raw NIST special database 19~\citep{Grother-1995}. It has
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
736 \{651668 / 80000 / 82587\} \{training / validation / test\} examples, containing
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
737 upper case, lower case, and digits.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
738 \vspace*{-1mm}
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parents:
diff changeset
739
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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diff changeset
740 %\item
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
741 {\bf P07.} This dataset of upper case, lower case and digit images
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
742 is obtained by taking raw characters from all four of the above sources
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
743 and sending them through the transformation pipeline described in section \ref{s:perturbations}.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
744 For each new example to generate, a data source is selected with probability $10\%$ from the fonts,
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
745 $25\%$ from the captchas, $25\%$ from the OCR data and $40\%$ from NIST. We apply all the transformations in the
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
746 order given above, and for each of them we sample uniformly a \emph{complexity} in the range $[0,0.7]$.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
747 It has \{81920000 / 80000 / 20000\} \{training / validation / test\} examples.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
748 \vspace*{-1mm}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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diff changeset
749
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
750 %\item
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
751 {\bf NISTP.} This one is equivalent to P07 (complexity parameter of $0.7$ with the same proportions of data sources)
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
752 except that we only apply
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
753 transformations from slant to pinch. Therefore, the character is
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
754 transformed but no additional noise is added to the image, giving images
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
755 closer to the NIST dataset.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
756 It has \{81,920,000 / 80,000 / 20,000\} \{training / validation / test\} examples
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
757 obtained from the corresponding NIST sets plus other sources.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
758 %\end{itemize}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
759
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
760 \vspace*{-3mm}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
761 \subsection{Models and their Hyperparameters}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
762 \vspace*{-2mm}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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diff changeset
763
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
764 The experiments are performed using MLPs (with a single
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
765 hidden layer) and deep SDAs.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
766 \emph{Hyper-parameters are selected based on the {\bf NISTP} validation set error.}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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diff changeset
767
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
768 {\bf Multi-Layer Perceptrons (MLP).}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
769 Whereas previous work had compared deep architectures to both shallow MLPs and
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
770 SVMs, we only compared to MLPs here because of the very large datasets used
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
771 (making the use of SVMs computationally challenging because of their quadratic
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
772 scaling behavior). Preliminary experiments on training SVMs (libSVM) with subsets of the training
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
773 set allowing the program to fit in memory yielded substantially worse results
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
774 than those obtained with MLPs. For training on nearly a hundred million examples
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
775 (with the perturbed data), the MLPs and SDA are much more convenient than
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
776 classifiers based on kernel methods.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
777 The MLP has a single hidden layer with $\tanh$ activation functions, and softmax (normalized
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
778 exponentials) on the output layer for estimating $P(class | image)$.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
779 The number of hidden units is taken in $\{300,500,800,1000,1500\}$.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
780 Training examples are presented in minibatches of size 20. A constant learning
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
781 rate was chosen among $\{0.001, 0.01, 0.025, 0.075, 0.1, 0.5\}$.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
782 %through preliminary experiments (measuring performance on a validation set),
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
783 %and $0.1$ (which was found to work best) was then selected for optimizing on
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
784 %the whole training sets.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
785 \vspace*{-1mm}
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parents:
diff changeset
786
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
787
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
788 {\bf Stacked Denoising Auto-encoders (SDA).}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
789 Various auto-encoder variants and Restricted Boltzmann Machines (RBMs)
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
790 can be used to initialize the weights of each layer of a deep MLP (with many hidden
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
791 layers)~\citep{Hinton06,ranzato-07-small,Bengio-nips-2006},
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
792 apparently setting parameters in the
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
793 basin of attraction of supervised gradient descent yielding better
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
794 generalization~\citep{Erhan+al-2010}. This initial {\em unsupervised
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
795 pre-training phase} uses all of the training images but not the training labels.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
796 Each layer is trained in turn to produce a new representation of its input
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
797 (starting from the raw pixels).
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
798 It is hypothesized that the
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
799 advantage brought by this procedure stems from a better prior,
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
800 on the one hand taking advantage of the link between the input
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
801 distribution $P(x)$ and the conditional distribution of interest
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
802 $P(y|x)$ (like in semi-supervised learning), and on the other hand
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
803 taking advantage of the expressive power and bias implicit in the
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
804 deep architecture (whereby complex concepts are expressed as
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
805 compositions of simpler ones through a deep hierarchy).
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
806
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
807 \begin{figure}[ht]
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
808 \vspace*{-2mm}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
809 \centerline{\resizebox{0.8\textwidth}{!}{\includegraphics{images/denoising_autoencoder_small.pdf}}}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
810 \vspace*{-2mm}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
811 \caption{Illustration of the computations and training criterion for the denoising
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
812 auto-encoder used to pre-train each layer of the deep architecture. Input $x$ of
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
813 the layer (i.e. raw input or output of previous layer)
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
814 s corrupted into $\tilde{x}$ and encoded into code $y$ by the encoder $f_\theta(\cdot)$.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
815 The decoder $g_{\theta'}(\cdot)$ maps $y$ to reconstruction $z$, which
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
816 is compared to the uncorrupted input $x$ through the loss function
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
817 $L_H(x,z)$, whose expected value is approximately minimized during training
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
818 by tuning $\theta$ and $\theta'$.}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
819 \label{fig:da}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
820 \vspace*{-2mm}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
821 \end{figure}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
822
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
823 Here we chose to use the Denoising
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
824 Auto-encoder~\citep{VincentPLarochelleH2008} as the building block for
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
825 these deep hierarchies of features, as it is simple to train and
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
826 explain (see Figure~\ref{fig:da}, as well as
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
827 tutorial and code there: {\tt http://deeplearning.net/tutorial}),
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
828 provides efficient inference, and yielded results
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
829 comparable or better than RBMs in series of experiments
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
830 \citep{VincentPLarochelleH2008-very-small}. It really corresponds to a Gaussian
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
831 RBM trained by a Score Matching criterion~\cite{Vincent-SM-2010}.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
832 During training, a Denoising
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
833 Auto-encoder is presented with a stochastically corrupted version
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
834 of the input and trained to reconstruct the uncorrupted input,
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
835 forcing the hidden units to represent the leading regularities in
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
836 the data. Here we use the random binary masking corruption
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
837 (which sets to 0 a random subset of the inputs).
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
838 Once it is trained, in a purely unsupervised way,
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
839 its hidden units' activations can
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
840 be used as inputs for training a second one, etc.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
841 After this unsupervised pre-training stage, the parameters
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
842 are used to initialize a deep MLP, which is fine-tuned by
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
843 the same standard procedure used to train them (see previous section).
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
844 The SDA hyper-parameters are the same as for the MLP, with the addition of the
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
845 amount of corruption noise (we used the masking noise process, whereby a
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
846 fixed proportion of the input values, randomly selected, are zeroed), and a
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
847 separate learning rate for the unsupervised pre-training stage (selected
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
848 from the same above set). The fraction of inputs corrupted was selected
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
849 among $\{10\%, 20\%, 50\%\}$. Another hyper-parameter is the number
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
850 of hidden layers but it was fixed to 3 based on previous work with
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
851 SDAs on MNIST~\citep{VincentPLarochelleH2008-very-small}. The size of the hidden
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
852 layers was kept constant across hidden layers, and the best results
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
853 were obtained with the largest values that we could experiment
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
854 with given our patience, with 1000 hidden units.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
855
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
856 \vspace*{-1mm}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
857
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
858 \begin{figure}[ht]
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
859 \vspace*{-2mm}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
860 \centerline{\resizebox{.99\textwidth}{!}{\includegraphics{images/error_rates_charts.pdf}}}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
861 \vspace*{-3mm}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
862 \caption{SDAx are the {\bf deep} models. Error bars indicate a 95\% confidence interval. 0 indicates that the model was trained
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
863 on NIST, 1 on NISTP, and 2 on P07. Left: overall results
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
864 of all models, on NIST and NISTP test sets.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
865 Right: error rates on NIST test digits only, along with the previous results from
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
866 literature~\citep{Granger+al-2007,Cortes+al-2000,Oliveira+al-2002-short,Milgram+al-2005}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
867 respectively based on ART, nearest neighbors, MLPs, and SVMs.}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
868 \label{fig:error-rates-charts}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
869 \vspace*{-2mm}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
870 \end{figure}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
871
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
872
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
873 \begin{figure}[ht]
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
874 \vspace*{-3mm}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
875 \centerline{\resizebox{.99\textwidth}{!}{\includegraphics{images/improvements_charts.pdf}}}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
876 \vspace*{-3mm}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
877 \caption{Relative improvement in error rate due to self-taught learning.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
878 Left: Improvement (or loss, when negative)
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
879 induced by out-of-distribution examples (perturbed data).
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
880 Right: Improvement (or loss, when negative) induced by multi-task
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
881 learning (training on all classes and testing only on either digits,
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
882 upper case, or lower-case). The deep learner (SDA) benefits more from
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
883 both self-taught learning scenarios, compared to the shallow MLP.}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
884 \label{fig:improvements-charts}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
885 \vspace*{-2mm}
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parents:
diff changeset
886 \end{figure}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
887
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
888 \section{Experimental Results}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
889 \vspace*{-2mm}
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diff changeset
890
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diff changeset
891 %\vspace*{-1mm}
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parents:
diff changeset
892 %\subsection{SDA vs MLP vs Humans}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
893 %\vspace*{-1mm}
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parents:
diff changeset
894 The models are either trained on NIST (MLP0 and SDA0),
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
895 NISTP (MLP1 and SDA1), or P07 (MLP2 and SDA2), and tested
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
896 on either NIST, NISTP or P07 (regardless of the data set used for training),
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
897 either on the 62-class task
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
898 or on the 10-digits task. Training time (including about half
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
899 for unsupervised pre-training, for DAs) on the larger
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
900 datasets is around one day on a GPU (GTX 285).
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
901 Figure~\ref{fig:error-rates-charts} summarizes the results obtained,
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
902 comparing humans, the three MLPs (MLP0, MLP1, MLP2) and the three SDAs (SDA0, SDA1,
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
903 SDA2), along with the previous results on the digits NIST special database
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
904 19 test set from the literature, respectively based on ARTMAP neural
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
905 networks ~\citep{Granger+al-2007}, fast nearest-neighbor search
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
906 ~\citep{Cortes+al-2000}, MLPs ~\citep{Oliveira+al-2002-short}, and SVMs
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
907 ~\citep{Milgram+al-2005}.% More detailed and complete numerical results
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
908 %(figures and tables, including standard errors on the error rates) can be
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
909 %found in Appendix.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
910 The deep learner not only outperformed the shallow ones and
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
911 previously published performance (in a statistically and qualitatively
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
912 significant way) but when trained with perturbed data
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
913 reaches human performance on both the 62-class task
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
914 and the 10-class (digits) task.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
915 17\% error (SDA1) or 18\% error (humans) may seem large but a large
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
916 majority of the errors from humans and from SDA1 are from out-of-context
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
917 confusions (e.g. a vertical bar can be a ``1'', an ``l'' or an ``L'', and a
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
918 ``c'' and a ``C'' are often indistinguishible).
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
919
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
920 In addition, as shown in the left of
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
921 Figure~\ref{fig:improvements-charts}, the relative improvement in error
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
922 rate brought by self-taught learning is greater for the SDA, and these
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
923 differences with the MLP are statistically and qualitatively
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
924 significant.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
925 The left side of the figure shows the improvement to the clean
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
926 NIST test set error brought by the use of out-of-distribution examples
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
927 (i.e. the perturbed examples examples from NISTP or P07),
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
928 over the models trained exclusively on NIST (respectively SDA0 and MLP0).
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
929 Relative percent change is measured by taking
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
930 $100 \% \times$ (original model's error / perturbed-data model's error - 1).
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
931 The right side of
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
932 Figure~\ref{fig:improvements-charts} shows the relative improvement
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
933 brought by the use of a multi-task setting, in which the same model is
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
934 trained for more classes than the target classes of interest (i.e. training
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
935 with all 62 classes when the target classes are respectively the digits,
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
936 lower-case, or upper-case characters). Again, whereas the gain from the
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
937 multi-task setting is marginal or negative for the MLP, it is substantial
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
938 for the SDA. Note that to simplify these multi-task experiments, only the original
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
939 NIST dataset is used. For example, the MLP-digits bar shows the relative
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
940 percent improvement in MLP error rate on the NIST digits test set
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
941 is $100\% \times$ (single-task
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
942 model's error / multi-task model's error - 1). The single-task model is
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
943 trained with only 10 outputs (one per digit), seeing only digit examples,
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
944 whereas the multi-task model is trained with 62 outputs, with all 62
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
945 character classes as examples. Hence the hidden units are shared across
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
946 all tasks. For the multi-task model, the digit error rate is measured by
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
947 comparing the correct digit class with the output class associated with the
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
948 maximum conditional probability among only the digit classes outputs. The
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
949 setting is similar for the other two target classes (lower case characters
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
950 and upper case characters). Note however that some types of perturbations
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
951 (NISTP) help more than others (P07) when testing on the clean images.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
952 %%\vspace*{-1mm}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
953 %\subsection{Perturbed Training Data More Helpful for SDA}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
954 %\vspace*{-1mm}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
955
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
956 %\vspace*{-1mm}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
957 %\subsection{Multi-Task Learning Effects}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
958 %\vspace*{-1mm}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
959
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
960 \iffalse
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
961 As previously seen, the SDA is better able to benefit from the
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
962 transformations applied to the data than the MLP. In this experiment we
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
963 define three tasks: recognizing digits (knowing that the input is a digit),
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
964 recognizing upper case characters (knowing that the input is one), and
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
965 recognizing lower case characters (knowing that the input is one). We
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
966 consider the digit classification task as the target task and we want to
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
967 evaluate whether training with the other tasks can help or hurt, and
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
968 whether the effect is different for MLPs versus SDAs. The goal is to find
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
969 out if deep learning can benefit more (or less) from multiple related tasks
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
970 (i.e. the multi-task setting) compared to a corresponding purely supervised
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
971 shallow learner.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
972
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
973 We use a single hidden layer MLP with 1000 hidden units, and a SDA
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
974 with 3 hidden layers (1000 hidden units per layer), pre-trained and
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
975 fine-tuned on NIST.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
976
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
977 Our results show that the MLP benefits marginally from the multi-task setting
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
978 in the case of digits (5\% relative improvement) but is actually hurt in the case
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
979 of characters (respectively 3\% and 4\% worse for lower and upper class characters).
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
980 On the other hand the SDA benefited from the multi-task setting, with relative
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
981 error rate improvements of 27\%, 15\% and 13\% respectively for digits,
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
982 lower and upper case characters, as shown in Table~\ref{tab:multi-task}.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
983 \fi
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
984
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
985
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
986 \vspace*{-2mm}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
987 \section{Conclusions and Discussion}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
988 \vspace*{-2mm}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
989
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
990 We have found that the self-taught learning framework is more beneficial
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
991 to a deep learner than to a traditional shallow and purely
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
992 supervised learner. More precisely,
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
993 the answers are positive for all the questions asked in the introduction.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
994 %\begin{itemize}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
995
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
996 $\bullet$ %\item
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
997 {\bf Do the good results previously obtained with deep architectures on the
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
998 MNIST digits generalize to a much larger and richer (but similar)
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
999 dataset, the NIST special database 19, with 62 classes and around 800k examples}?
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1000 Yes, the SDA {\em systematically outperformed the MLP and all the previously
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1001 published results on this dataset} (the ones that we are aware of), {\em in fact reaching human-level
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1002 performance} at around 17\% error on the 62-class task and 1.4\% on the digits,
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1003 and beating previously published results on the same data.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1004
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1005 $\bullet$ %\item
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1006 {\bf To what extent do self-taught learning scenarios help deep learners,
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1007 and do they help them more than shallow supervised ones}?
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1008 We found that distorted training examples not only made the resulting
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1009 classifier better on similarly perturbed images but also on
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1010 the {\em original clean examples}, and more importantly and more novel,
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1011 that deep architectures benefit more from such {\em out-of-distribution}
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1012 examples. MLPs were helped by perturbed training examples when tested on perturbed input
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1013 images (65\% relative improvement on NISTP)
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1014 but only marginally helped (5\% relative improvement on all classes)
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1015 or even hurt (10\% relative loss on digits)
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1016 with respect to clean examples. On the other hand, the deep SDAs
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1017 were significantly boosted by these out-of-distribution examples.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1018 Similarly, whereas the improvement due to the multi-task setting was marginal or
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1019 negative for the MLP (from +5.6\% to -3.6\% relative change),
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1020 it was quite significant for the SDA (from +13\% to +27\% relative change),
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1021 which may be explained by the arguments below.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1022 %\end{itemize}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1023
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
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1024 In the original self-taught learning framework~\citep{RainaR2007}, the
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1025 out-of-sample examples were used as a source of unsupervised data, and
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1026 experiments showed its positive effects in a \emph{limited labeled data}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1027 scenario. However, many of the results by \citet{RainaR2007} (who used a
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1028 shallow, sparse coding approach) suggest that the {\em relative gain of self-taught
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1029 learning vs ordinary supervised learning} diminishes as the number of labeled examples increases.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1030 We note instead that, for deep
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
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1031 architectures, our experiments show that such a positive effect is accomplished
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1032 even in a scenario with a \emph{large number of labeled examples},
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parents:
diff changeset
1033 i.e., here, the relative gain of self-taught learning and
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1034 out-of-distribution examples is probably preserved
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1035 in the asymptotic regime. However, note that in our perturbation experiments
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1036 (but not in our multi-task experiments),
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1037 even the out-of-distribution examples are labeled, unlike in the
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1038 earlier self-taught learning experiments~\citep{RainaR2007}.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1039
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
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1040 {\bf Why would deep learners benefit more from the self-taught learning framework}?
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1041 The key idea is that the lower layers of the predictor compute a hierarchy
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1042 of features that can be shared across tasks or across variants of the
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1043 input distribution. A theoretical analysis of generalization improvements
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1044 due to sharing of intermediate features across tasks already points
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1045 towards that explanation~\cite{baxter95a}.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1046 Intermediate features that can be used in different
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1047 contexts can be estimated in a way that allows to share statistical
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1048 strength. Features extracted through many levels are more likely to
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1049 be more abstract and more invariant to some of the factors of variation
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1050 in the underlying distribution (as the experiments in~\citet{Goodfellow2009} suggest),
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1051 increasing the likelihood that they would be useful for a larger array
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1052 of tasks and input conditions.
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1053 Therefore, we hypothesize that both depth and unsupervised
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1054 pre-training play a part in explaining the advantages observed here, and future
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1055 experiments could attempt at teasing apart these factors.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1056 And why would deep learners benefit from the self-taught learning
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1057 scenarios even when the number of labeled examples is very large?
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1058 We hypothesize that this is related to the hypotheses studied
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1059 in~\citet{Erhan+al-2010}. In~\citet{Erhan+al-2010}
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1060 it was found that online learning on a huge dataset did not make the
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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diff changeset
1061 advantage of the deep learning bias vanish, and a similar phenomenon
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1062 may be happening here. We hypothesize that unsupervised pre-training
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1063 of a deep hierarchy with self-taught learning initializes the
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1064 model in the basin of attraction of supervised gradient descent
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1065 that corresponds to better generalization. Furthermore, such good
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1066 basins of attraction are not discovered by pure supervised learning
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1067 (with or without self-taught settings), and more labeled examples
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1068 does not allow the model to go from the poorer basins of attraction discovered
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1069 by the purely supervised shallow models to the kind of better basins associated
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1070 with deep learning and self-taught learning.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1071
51213beaed8b draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1072 A Flash demo of the recognizer (where both the MLP and the SDA can be compared)
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff changeset
1073 can be executed on-line at {\tt http://deep.host22.com}.
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
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1074
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1075 \newpage
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1076 {
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1077 \bibliography{strings,strings-short,strings-shorter,ift6266_ml,aigaion-shorter,specials}
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1078 %\bibliographystyle{plainnat}
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1079 \bibliographystyle{unsrtnat}
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1080 %\bibliographystyle{apalike}
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1081 }
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1082
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1083
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1084 \end{document}