Mercurial > ift6266
annotate writeup/nips2010_submission.tex @ 557:17d16700e0c8
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author | Yoshua Bengio <bengioy@iro.umontreal.ca> |
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date | Thu, 03 Jun 2010 09:17:01 -0400 |
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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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523 | 11 %\setlength\parindent{0mm} |
12 | |
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13 \title{Deep Self-Taught Learning for Handwritten Character Recognition} |
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14 \author{The IFT6266 Gang} |
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16 \begin{document} |
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17 |
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18 %\makeanontitle |
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19 \maketitle |
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20 |
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21 \vspace*{-2mm} |
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22 \begin{abstract} |
554 | 23 Recent theoretical and empirical work in statistical machine learning has demonstrated the importance of learning algorithms for deep architectures, i.e., function classes obtained by composing multiple non-linear transformations. Self-taught learning (exploiting unlabeled examples or examples from other distributions) has already been applied to deep learners, but mostly to show the advantage of unlabeled examples. Here we explore the advantage brought by {\em out-of-distribution examples} and show that {\em deep learners benefit more from them than a corresponding shallow learner}, in the area of handwritten character recognition. In fact, we show that they reach human-level performance on both handwritten digit classification and 62-class handwritten character recognition. For this purpose we developed a powerful generator of stochastic variations and noise processes for character images, including not only affine transformations but also slant, local elastic deformations, changes in thickness, background images, grey level changes, contrast, occlusion, and various types of noise. The out-of-distribution examples are obtained from these highly distorted images or by including examples of object classes different from those in the target test set. |
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24 \end{abstract} |
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25 \vspace*{-3mm} |
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26 |
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27 \section{Introduction} |
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28 \vspace*{-1mm} |
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29 |
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30 Deep Learning has emerged as a promising new area of research in |
469 | 31 statistical machine learning (see~\citet{Bengio-2009} for a review). |
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32 Learning algorithms for deep architectures are centered on the learning |
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33 of useful representations of data, which are better suited to the task at hand. |
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34 This is in great part inspired by observations of the mammalian visual cortex, |
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35 which consists of a chain of processing elements, each of which is associated with a |
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36 different representation of the raw visual input. In fact, |
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37 it was found recently that the features learnt in deep architectures resemble |
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38 those observed in the first two of these stages (in areas V1 and V2 |
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39 of visual cortex)~\citep{HonglakL2008}, and that they become more and |
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40 more invariant to factors of variation (such as camera movement) in |
501 | 41 higher layers~\citep{Goodfellow2009}. |
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42 Learning a hierarchy of features increases the |
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43 ease and practicality of developing representations that are at once |
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44 tailored to specific tasks, yet are able to borrow statistical strength |
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45 from other related tasks (e.g., modeling different kinds of objects). Finally, learning the |
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46 feature representation can lead to higher-level (more abstract, more |
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47 general) features that are more robust to unanticipated sources of |
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48 variance extant in real data. |
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49 |
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50 Whereas a deep architecture can in principle be more powerful than a |
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51 shallow one in terms of representation, depth appears to render the |
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52 training problem more difficult in terms of optimization and local minima. |
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53 It is also only recently that successful algorithms were proposed to |
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54 overcome some of these difficulties. All are based on unsupervised |
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55 learning, often in an greedy layer-wise ``unsupervised pre-training'' |
469 | 56 stage~\citep{Bengio-2009}. One of these layer initialization techniques, |
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57 applied here, is the Denoising |
547 | 58 Auto-encoder~(DA)~\citep{VincentPLarochelleH2008-very-small} (see Figure~\ref{fig:da}), |
59 which | |
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60 performed similarly or better than previously proposed Restricted Boltzmann |
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61 Machines in terms of unsupervised extraction of a hierarchy of features |
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62 useful for classification. The principle is that each layer starting from |
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63 the bottom is trained to encode its input (the output of the previous |
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64 layer) and to reconstruct it from a corrupted version. After this |
547 | 65 unsupervised initialization, the stack of DAs can be |
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66 converted into a deep supervised feedforward neural network and fine-tuned by |
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67 stochastic gradient descent. |
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68 |
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69 Self-taught learning~\citep{RainaR2007} is a paradigm that combines principles |
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70 of semi-supervised and multi-task learning: the learner can exploit examples |
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71 that are unlabeled and/or come from a distribution different from the target |
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72 distribution, e.g., from other classes than those of interest. |
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73 It has already been shown that deep learners can clearly take advantage of |
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74 unsupervised learning and unlabeled examples~\citep{Bengio-2009,WestonJ2008-small}, |
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75 but more needs to be done to explore the impact |
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76 of {\em out-of-distribution} examples and of the multi-task setting |
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77 (one exception is~\citep{CollobertR2008}, which uses very different kinds |
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78 of learning algorithms). In particular the {\em relative |
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79 advantage} of deep learning for these settings has not been evaluated. |
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80 The hypothesis discussed in the conclusion is that a deep hierarchy of features |
512 | 81 may be better able to provide sharing of statistical strength |
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82 between different regions in input space or different tasks. |
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83 % |
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84 In this paper we ask the following questions: |
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85 |
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86 %\begin{enumerate} |
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87 $\bullet$ %\item |
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88 Do the good results previously obtained with deep architectures on the |
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89 MNIST digit images generalize to the setting of a much larger and richer (but similar) |
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90 dataset, the NIST special database 19, with 62 classes and around 800k examples? |
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91 |
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92 $\bullet$ %\item |
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93 To what extent does the perturbation of input images (e.g. adding |
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94 noise, affine transformations, background images) make the resulting |
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95 classifiers better not only on similarly perturbed images but also on |
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96 the {\em original clean examples}? |
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97 |
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98 $\bullet$ %\item |
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99 Do deep architectures {\em benefit more from such out-of-distribution} |
469 | 100 examples, i.e. do they benefit more from the self-taught learning~\citep{RainaR2007} framework? |
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101 |
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102 $\bullet$ %\item |
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103 Similarly, does the feature learning step in deep learning algorithms benefit more |
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104 from training with moderately different classes (i.e. a multi-task learning scenario) than |
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105 a corresponding shallow and purely supervised architecture? |
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106 %\end{enumerate} |
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107 |
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108 Our experimental results provide positive evidence towards all of these questions. |
547 | 109 To achieve these results, we introduce in the next section a sophisticated system |
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110 for stochastically transforming character images and then explain the methodology. |
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111 The conclusion discusses |
547 | 112 the more general question of why deep learners may benefit so much from |
113 the self-taught learning framework. | |
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114 |
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115 \vspace*{-1mm} |
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116 \section{Perturbation and Transformation of Character Images} |
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117 \label{s:perturbations} |
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118 \vspace*{-1mm} |
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119 |
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120 \begin{wrapfigure}[8]{l}{0.15\textwidth} |
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121 %\begin{minipage}[b]{0.14\linewidth} |
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122 \vspace*{-5mm} |
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123 \begin{center} |
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124 \includegraphics[scale=.4]{images/Original.PNG}\\ |
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125 {\bf Original} |
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126 \end{center} |
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127 \end{wrapfigure} |
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128 %\vspace{0.7cm} |
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129 %\end{minipage}% |
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130 %\hspace{0.3cm}\begin{minipage}[b]{0.86\linewidth} |
467 | 131 This section describes the different transformations we used to stochastically |
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132 transform source images such as the one on the left |
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133 in order to obtain data from a larger distribution which |
547 | 134 covers a domain substantially larger than the clean characters distribution from |
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135 which we start. |
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136 Although character transformations have been used before to |
547 | 137 improve character recognizers, this effort is on a large scale both |
138 in number of classes and in the complexity of the transformations, hence | |
139 in the complexity of the learning task. | |
140 More details can | |
469 | 141 be found in this technical report~\citep{ift6266-tr-anonymous}. |
464
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142 The code for these transformations (mostly python) is available at |
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143 {\tt http://anonymous.url.net}. All the modules in the pipeline share |
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144 a global control parameter ($0 \le complexity \le 1$) that allows one to modulate the |
467 | 145 amount of deformation or noise introduced. |
146 There are two main parts in the pipeline. The first one, | |
147 from slant to pinch below, performs transformations. The second | |
148 part, from blur to contrast, adds different kinds of noise. | |
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149 %\end{minipage} |
464
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150 |
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151 \vspace*{1mm} |
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152 %\subsection{Transformations} |
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153 {\large\bf 2.1 Transformations} |
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154 \vspace*{1mm} |
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155 |
501 | 156 |
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157 \begin{wrapfigure}[7]{l}{0.15\textwidth} |
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158 %\begin{minipage}[b]{0.14\linewidth} |
551
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159 %\centering |
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160 \begin{center} |
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161 \vspace*{-5mm} |
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162 \includegraphics[scale=.4]{images/Thick_only.PNG}\\ |
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163 {\bf Thickness} |
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164 \end{center} |
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165 %\vspace{.6cm} |
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166 %\end{minipage}% |
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167 %\hspace{0.3cm}\begin{minipage}[b]{0.86\linewidth} |
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168 \end{wrapfigure} |
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169 To change character {\bf thickness}, morphological operators of dilation and erosion~\citep{Haralick87,Serra82} |
467 | 170 are applied. The neighborhood of each pixel is multiplied |
171 element-wise with a {\em structuring element} matrix. | |
172 The pixel value is replaced by the maximum or the minimum of the resulting | |
173 matrix, respectively for dilation or erosion. Ten different structural elements with | |
174 increasing dimensions (largest is $5\times5$) were used. For each image, | |
175 randomly sample the operator type (dilation or erosion) with equal probability and one structural | |
541 | 176 element from a subset of the $n=round(m \times complexity)$ smallest structuring elements |
177 where $m=10$ for dilation and $m=6$ for erosion (to avoid completely erasing thin characters). | |
178 A neutral element (no transformation) | |
179 is always present in the set. is applied. | |
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180 %\vspace{.4cm} |
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181 %\end{minipage} |
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182 %\vspace{-.7cm} |
523 | 183 |
551
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184 \begin{minipage}[b]{0.14\linewidth} |
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185 \centering |
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186 \includegraphics[scale=.4]{images/Slant_only.PNG}\\ |
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187 {\bf Slant} |
551
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188 \end{minipage}% |
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189 \hspace{0.3cm}\begin{minipage}[b]{0.83\linewidth} |
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190 %\centering |
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191 %\vspace*{-15mm} |
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192 To produce {\bf slant}, each row of the image is shifted |
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193 proportionally to its height: $shift = round(slant \times height)$. |
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194 $slant \sim U[-complexity,complexity]$. |
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195 \vspace{1.5cm} |
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196 \end{minipage} |
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197 %\vspace*{-4mm} |
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198 |
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199 %\begin{minipage}[b]{0.14\linewidth} |
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200 %\centering |
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201 \begin{wrapfigure}[8]{l}{0.15\textwidth} |
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202 \vspace*{-6mm} |
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203 \begin{center} |
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204 \includegraphics[scale=.4]{images/Affine_only.PNG}\\ |
557
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205 {\bf Affine Transformation} |
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206 \end{center} |
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207 \end{wrapfigure} |
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208 %\end{minipage}% |
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209 %\hspace{0.3cm}\begin{minipage}[b]{0.86\linewidth} |
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210 A $2 \times 3$ {\bf affine transform} matrix (with |
555
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211 parameters $(a,b,c,d,e,f)$) is sampled according to the $complexity$. |
541 | 212 Output pixel $(x,y)$ takes the value of input pixel |
213 nearest to $(ax+by+c,dx+ey+f)$, | |
214 producing scaling, translation, rotation and shearing. | |
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215 Marginal distributions of $(a,b,c,d,e,f)$ have been tuned to |
550
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216 forbid large rotations (not to confuse classes) but to give good |
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217 variability of the transformation: $a$ and $d$ $\sim U[1-3 |
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218 complexity,1+3\,complexity]$, $b$ and $e$ $\sim[-3 \,complexity,3\, |
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219 complexity]$ and $c$ and $f$ $\sim U[-4 \,complexity, 4 \, |
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220 complexity]$.\\ |
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221 %\end{minipage} |
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222 |
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223 \vspace*{-4.5mm} |
523 | 224 |
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225 \begin{minipage}[t]{\linewidth} |
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226 \begin{wrapfigure}[7]{l}{0.15\textwidth} |
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227 %\hspace*{-8mm}\begin{minipage}[b]{0.25\linewidth} |
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228 %\centering |
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229 \begin{center} |
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230 \vspace*{-4mm} |
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231 \includegraphics[scale=.4]{images/Localelasticdistorsions_only.PNG}\\ |
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232 {\bf Local Elastic} |
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233 \end{center} |
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234 \end{wrapfigure} |
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235 %\end{minipage}% |
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236 %\hspace{-3mm}\begin{minipage}[b]{0.85\linewidth} |
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237 %\vspace*{-20mm} |
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238 The {\bf local elastic} deformation |
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239 module induces a ``wiggly'' effect in the image, following~\citet{SimardSP03-short}, |
467 | 240 which provides more details. |
541 | 241 The intensity of the displacement fields is given by |
242 $\alpha = \sqrt[3]{complexity} \times 10.0$, which are | |
243 convolved with a Gaussian 2D kernel (resulting in a blur) of | |
244 standard deviation $\sigma = 10 - 7 \times\sqrt[3]{complexity}$. | |
555
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245 %\vspace{.9cm} |
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246 \end{minipage} |
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247 |
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248 \vspace*{5mm} |
523 | 249 |
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250 %\begin{minipage}[b]{0.14\linewidth} |
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251 %\centering |
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252 \begin{wrapfigure}[7]{l}{0.15\textwidth} |
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253 \vspace*{-5mm} |
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254 \begin{center} |
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255 \includegraphics[scale=.4]{images/Pinch_only.PNG}\\ |
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256 {\bf Pinch} |
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257 \end{center} |
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258 \end{wrapfigure} |
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259 %\vspace{.6cm} |
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260 %\end{minipage}% |
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261 %\hspace{0.3cm}\begin{minipage}[b]{0.86\linewidth} |
557
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262 The {\bf pinch} module applies the ``Whirl and pinch'' GIMP filter with whirl was set to 0. |
541 | 263 A pinch is ``similar to projecting the image onto an elastic |
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264 surface and pressing or pulling on the center of the surface'' (GIMP documentation manual). |
555
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265 For a square input image, draw a radius-$r$ disk |
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266 around $C$. Any pixel $P$ belonging to |
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267 that disk has its value replaced by |
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268 the value of a ``source'' pixel in the original image, |
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269 on the line that goes through $C$ and $P$, but |
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270 at some other distance $d_2$. Define $d_1=distance(P,C) = sin(\frac{\pi{}d_1}{2r})^{-pinch} \times |
557
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271 d_1$, where $pinch$ is a parameter of the filter. |
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272 The actual value is given by bilinear interpolation considering the pixels |
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273 around the (non-integer) source position thus found. |
484
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274 Here $pinch \sim U[-complexity, 0.7 \times complexity]$. |
551
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275 %\vspace{1.5cm} |
555
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276 %\end{minipage} |
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277 |
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278 \vspace{2mm} |
484
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279 |
555
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280 {\large\bf 2.2 Injecting Noise} |
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281 %\subsection{Injecting Noise} |
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282 \vspace{2mm} |
484
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283 |
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284 %\vspace*{-.2cm} |
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285 \begin{minipage}[t]{0.14\linewidth} |
551
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286 \centering |
555
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287 \vspace*{-2mm} |
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288 \includegraphics[scale=.4]{images/Motionblur_only.PNG}\\ |
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289 {\bf Motion Blur} |
551
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290 \end{minipage}% |
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291 \hspace{0.3cm}\begin{minipage}[t]{0.83\linewidth} |
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292 %\vspace*{.5mm} |
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293 The {\bf motion blur} module is GIMP's ``linear motion blur'', which |
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294 has parameters $length$ and $angle$. The value of |
555
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295 a pixel in the final image is approximately the mean of the first $length$ pixels |
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296 found by moving in the $angle$ direction, |
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297 $angle \sim U[0,360]$ degrees, and $length \sim {\rm Normal}(0,(3 \times complexity)^2)$. |
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298 \vspace{5mm} |
551
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299 \end{minipage} |
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300 |
555
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301 \vspace*{1mm} |
523 | 302 |
555
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303 \begin{minipage}[t]{0.14\linewidth} |
551
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304 \centering |
555
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305 \includegraphics[scale=.4]{images/occlusion_only.PNG}\\ |
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306 {\bf Occlusion} |
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307 %\vspace{.5cm} |
551
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308 \end{minipage}% |
555
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309 \hspace{0.3cm}\begin{minipage}[t]{0.83\linewidth} |
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310 \vspace*{-18mm} |
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311 The {\bf occlusion} module selects a random rectangle from an {\em occluder} character |
544
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312 image and places it over the original {\em occluded} |
467 | 313 image. Pixels are combined by taking the max(occluder,occluded), |
517
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511
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314 closer to black. The rectangle corners |
467 | 315 are sampled so that larger complexity gives larger rectangles. |
316 The destination position in the occluded image are also sampled | |
544
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317 according to a normal distribution (more details in~\citet{ift6266-tr-anonymous}). |
557
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318 This module is skipped with probability 60\%. |
555
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319 %\vspace{7mm} |
551
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320 \end{minipage} |
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321 |
555
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322 \vspace*{1mm} |
523 | 323 |
555
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324 \begin{wrapfigure}[8]{l}{0.15\textwidth} |
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325 \vspace*{-6mm} |
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326 \begin{center} |
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327 %\begin{minipage}[t]{0.14\linewidth} |
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328 %\centering |
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329 \includegraphics[scale=.4]{images/Bruitgauss_only.PNG}\\ |
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330 {\bf Gaussian Smoothing} |
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331 \end{center} |
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332 \end{wrapfigure} |
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333 %\vspace{.5cm} |
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334 %\end{minipage}% |
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335 %\hspace{0.3cm}\begin{minipage}[t]{0.86\linewidth} |
557
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336 With the {\bf Gaussian smoothing} module, |
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337 different regions of the image are spatially smoothed by convolving |
554 | 338 the image with a symmetric Gaussian kernel of |
495 | 339 size and variance chosen uniformly in the ranges $[12,12 + 20 \times |
464
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340 complexity]$ and $[2,2 + 6 \times complexity]$. The result is normalized |
554 | 341 between $0$ and $1$. We also create a symmetric weighted averaging window, of the |
464
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342 kernel size, with maximum value at the center. For each image we sample |
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343 uniformly from $3$ to $3 + 10 \times complexity$ pixels that will be |
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344 averaging centers between the original image and the filtered one. We |
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345 initialize to zero a mask matrix of the image size. For each selected pixel |
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346 we add to the mask the averaging window centered to it. The final image is |
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347 computed from the following element-wise operation: $\frac{image + filtered |
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348 image \times mask}{mask+1}$. |
557
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349 This module is skipped with probability 75\%. |
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350 %\end{minipage} |
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351 |
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352 \newpage |
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353 |
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354 \vspace*{-9mm} |
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355 |
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356 %\hspace*{-3mm}\begin{minipage}[t]{0.18\linewidth} |
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357 %\centering |
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358 \begin{minipage}[t]{\linewidth} |
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359 \begin{wrapfigure}[7]{l}{0.15\textwidth} |
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360 \vspace*{-5mm} |
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361 \begin{center} |
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362 \includegraphics[scale=.4]{images/Permutpixel_only.PNG}\\ |
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363 {\small\bf Permute Pixels} |
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364 \end{center} |
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365 \end{wrapfigure} |
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366 %\end{minipage}% |
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|
367 %\hspace{-0cm}\begin{minipage}[t]{0.86\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
368 %\vspace*{-20mm} |
557
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encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
369 This module {\bf permutes neighbouring pixels}. It first selects |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
370 fraction $\frac{complexity}{3}$ of pixels randomly in the image. Each of them are then |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
371 sequentially exchanged with one other in as $V4$ neighbourhood. |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
372 This module is skipped with probability 80\%.\\ |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
373 \vspace*{1mm} |
551
8f365abf171d
separete the transmo image
Frederic Bastien <nouiz@nouiz.org>
parents:
550
diff
changeset
|
374 \end{minipage} |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
375 |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
376 \vspace{-3mm} |
523 | 377 |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
378 \begin{minipage}[t]{\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
379 \begin{wrapfigure}[7]{l}{0.15\textwidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
380 %\vspace*{-3mm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
381 \begin{center} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
382 %\hspace*{-3mm}\begin{minipage}[t]{0.18\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
383 %\centering |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
384 \vspace*{-5mm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
385 \includegraphics[scale=.4]{images/Distorsiongauss_only.PNG}\\ |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
386 {\small \bf Gauss. Noise} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
387 \end{center} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
388 \end{wrapfigure} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
389 %\end{minipage}% |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
390 %\hspace{0.3cm}\begin{minipage}[t]{0.86\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
391 \vspace*{12mm} |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
392 The {\bf Gaussian noise} module simply adds, to each pixel of the image independently, a |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
393 noise $\sim Normal(0,(\frac{complexity}{10})^2)$. |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
394 This module is skipped with probability 70\%. |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
395 %\vspace{1.1cm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
396 \end{minipage} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
397 |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
398 \vspace*{1.2cm} |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
399 |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
400 \begin{minipage}[t]{\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
401 \begin{minipage}[t]{0.14\linewidth} |
551
8f365abf171d
separete the transmo image
Frederic Bastien <nouiz@nouiz.org>
parents:
550
diff
changeset
|
402 \centering |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
403 \includegraphics[scale=.4]{images/background_other_only.png}\\ |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
404 {\small \bf Bg Image} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
405 \end{minipage}% |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
406 \hspace{0.3cm}\begin{minipage}[t]{0.83\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
407 \vspace*{-18mm} |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
408 Following~\citet{Larochelle-jmlr-2009}, the {\bf background image} module adds a random |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
409 background image behind the letter, from a randomly chosen natural image, |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
410 with contrast adjustments depending on $complexity$, to preserve |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
411 more or less of the original character image. |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
412 %\vspace{.8cm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
413 \end{minipage} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
414 \end{minipage} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
415 %\vspace{-.7cm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
416 |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
417 \begin{minipage}[t]{0.14\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
418 \centering |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
419 \includegraphics[scale=.4]{images/Poivresel_only.PNG}\\ |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
420 {\small \bf Salt \& Pepper} |
551
8f365abf171d
separete the transmo image
Frederic Bastien <nouiz@nouiz.org>
parents:
550
diff
changeset
|
421 \end{minipage}% |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
422 \hspace{0.3cm}\begin{minipage}[t]{0.83\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
423 \vspace*{-18mm} |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
424 The {\bf salt and pepper noise} module adds noise $\sim U[0,1]$ to random subsets of pixels. |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
425 The number of selected pixels is $0.2 \times complexity$. |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
426 This module is skipped with probability 75\%. |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
427 %\vspace{.9cm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
428 \end{minipage} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
429 %\vspace{-.7cm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
430 |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
431 \vspace{1mm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
432 |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
433 \begin{minipage}[t]{\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
434 \begin{wrapfigure}[7]{l}{0.14\textwidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
435 %\begin{minipage}[t]{0.14\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
436 %\centering |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
437 \begin{center} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
438 \vspace*{-4mm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
439 \hspace*{-1mm}\includegraphics[scale=.4]{images/Rature_only.PNG}\\ |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
440 {\bf Scratches} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
441 %\end{minipage}% |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
442 \end{center} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
443 \end{wrapfigure} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
444 %\hspace{0.3cm}\begin{minipage}[t]{0.86\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
445 %\vspace{.4cm} |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
446 The {\bf scratches} module places line-like white patches on the image. The |
517
0a5945249f2b
section 2, quick first pass
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
511
diff
changeset
|
447 lines are heavily transformed images of the digit ``1'' (one), chosen |
544
1cdfc17e890f
ca fitte maintenant
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
541
diff
changeset
|
448 at random among 500 such 1 images, |
467 | 449 randomly cropped and rotated by an angle $\sim Normal(0,(100 \times |
554 | 450 complexity)^2$ (in degrees), using bi-cubic interpolation. |
495 | 451 Two passes of a grey-scale morphological erosion filter |
467 | 452 are applied, reducing the width of the line |
453 by an amount controlled by $complexity$. | |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
454 This module is skipped with probability 85\%. The probabilities |
544
1cdfc17e890f
ca fitte maintenant
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
541
diff
changeset
|
455 of applying 1, 2, or 3 patches are (50\%,30\%,20\%). |
551
8f365abf171d
separete the transmo image
Frederic Bastien <nouiz@nouiz.org>
parents:
550
diff
changeset
|
456 \end{minipage} |
523 | 457 |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
458 \vspace*{2mm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
459 |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
460 \begin{minipage}[t]{0.25\linewidth} |
551
8f365abf171d
separete the transmo image
Frederic Bastien <nouiz@nouiz.org>
parents:
550
diff
changeset
|
461 \centering |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
462 \hspace*{-16mm}\includegraphics[scale=.4]{images/Contrast_only.PNG}\\ |
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
463 {\bf Grey Level \& Contrast} |
551
8f365abf171d
separete the transmo image
Frederic Bastien <nouiz@nouiz.org>
parents:
550
diff
changeset
|
464 \end{minipage}% |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
465 \hspace{-12mm}\begin{minipage}[t]{0.82\linewidth} |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
466 \vspace*{-18mm} |
557
17d16700e0c8
encore du visuel de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
555
diff
changeset
|
467 The {\bf grey level and contrast} module changes the contrast by changing grey levels, and may invert the image polarity (white |
544
1cdfc17e890f
ca fitte maintenant
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
541
diff
changeset
|
468 to black and black to white). The contrast is $C \sim U[1-0.85 \times complexity,1]$ |
1cdfc17e890f
ca fitte maintenant
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
541
diff
changeset
|
469 so the image is normalized into $[\frac{1-C}{2},1-\frac{1-C}{2}]$. The |
1cdfc17e890f
ca fitte maintenant
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
541
diff
changeset
|
470 polarity is inverted with probability 50\%. |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
471 %\vspace{.7cm} |
551
8f365abf171d
separete the transmo image
Frederic Bastien <nouiz@nouiz.org>
parents:
550
diff
changeset
|
472 \end{minipage} |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
473 \vspace{2mm} |
551
8f365abf171d
separete the transmo image
Frederic Bastien <nouiz@nouiz.org>
parents:
550
diff
changeset
|
474 |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
475 |
499
2b58eda9fc08
changements de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
495
diff
changeset
|
476 \iffalse |
523 | 477 \begin{figure}[ht] |
538 | 478 \centerline{\resizebox{.9\textwidth}{!}{\includegraphics{images/example_t.png}}}\\ |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
479 \caption{Illustration of the pipeline of stochastic |
519
eaa595ea2402
section 3 quickpass
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
518
diff
changeset
|
480 transformations applied to the image of a lower-case \emph{t} |
464
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481 (the upper left image). Each image in the pipeline (going from |
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482 left to right, first top line, then bottom line) shows the result |
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483 of applying one of the modules in the pipeline. The last image |
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484 (bottom right) is used as training example.} |
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485 \label{fig:pipeline} |
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486 \end{figure} |
499
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487 \fi |
464
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488 |
479
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489 |
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490 \vspace*{-2mm} |
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491 \section{Experimental Setup} |
484
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492 \vspace*{-1mm} |
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493 |
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494 Much previous work on deep learning had been performed on |
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495 the MNIST digits task~\citep{Hinton06,ranzato-07-small,Bengio-nips-2006,Salakhutdinov+Hinton-2009}, |
472 | 496 with 60~000 examples, and variants involving 10~000 |
553
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497 examples~\citep{Larochelle-jmlr-toappear-2008,VincentPLarochelleH2008}. |
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498 The focus here is on much larger training sets, from 10 times to |
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499 to 1000 times larger, and 62 classes. |
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500 |
535 | 501 The first step in constructing the larger datasets (called NISTP and P07) is to sample from |
499
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502 a {\em data source}: {\bf NIST} (NIST database 19), {\bf Fonts}, {\bf Captchas}, |
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503 and {\bf OCR data} (scanned machine printed characters). Once a character |
547 | 504 is sampled from one of these sources (chosen randomly), the second step is to |
505 apply a pipeline of transformations and/or noise processes described in section \ref{s:perturbations}. | |
464
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506 |
547 | 507 To provide a baseline of error rate comparison we also estimate human performance |
508 on both the 62-class task and the 10-class digits task. | |
535 | 509 We compare the best MLPs against |
510 the best SDAs (both models' hyper-parameters are selected to minimize the validation set error), | |
530
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511 along with a comparison against a precise estimate |
502
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512 of human performance obtained via Amazon's Mechanical Turk (AMT) |
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513 service (http://mturk.com). |
502
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514 AMT users are paid small amounts |
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515 of money to perform tasks for which human intelligence is required. |
522
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521
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516 Mechanical Turk has been used extensively in natural language processing and vision. |
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521
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517 %processing \citep{SnowEtAl2008} and vision |
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518 %\citep{SorokinAndForsyth2008,whitehill09}. |
530
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519 AMT users were presented |
553
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520 with 10 character images (from a test set) and asked to choose 10 corresponding ASCII |
502
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521 characters. They were forced to make a hard choice among the |
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522 62 or 10 character classes (all classes or digits only). |
553
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523 80 subjects classified 2500 images per (dataset,task) pair, |
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524 with the guarantee that 3 different subjects classified each image, allowing |
550
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525 us to estimate inter-human variability (e.g a standard error of 0.1\% |
553
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526 on the average 18.2\% error done by humans on the 62-class task NIST test set). |
502
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527 |
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528 \vspace*{-3mm} |
472 | 529 \subsection{Data Sources} |
553
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530 \vspace*{-2mm} |
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531 |
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532 %\begin{itemize} |
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533 %\item |
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534 {\bf NIST.} |
501 | 535 Our main source of characters is the NIST Special Database 19~\citep{Grother-1995}, |
472 | 536 widely used for training and testing character |
516
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537 recognition systems~\citep{Granger+al-2007,Cortes+al-2000,Oliveira+al-2002-short,Milgram+al-2005}. |
519
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538 The dataset is composed of 814255 digits and characters (upper and lower cases), with hand checked classifications, |
464
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539 extracted from handwritten sample forms of 3600 writers. The characters are labelled by one of the 62 classes |
519
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540 corresponding to ``0''-``9'',``A''-``Z'' and ``a''-``z''. The dataset contains 8 parts (partitions) of varying complexity. |
534 | 541 The fourth partition (called $hsf_4$, 82587 examples), |
542 experimentally recognized to be the most difficult one, is the one recommended | |
519
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543 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} |
534 | 544 for that purpose. We randomly split the remainder (731668 examples) into a training set and a validation set for |
545 model selection. | |
464
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546 The performances reported by previous work on that dataset mostly use only the digits. |
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547 Here we use all the classes both in the training and testing phase. This is especially |
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|
548 useful to estimate the effect of a multi-task setting. |
553
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549 The distribution of the classes in the NIST training and test sets differs |
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550 substantially, with relatively many more digits in the test set, and a more uniform distribution |
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551 of letters in the test set (where the letters are distributed |
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552 more like in natural text). |
549 | 553 \vspace*{-1mm} |
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554 |
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555 %\item |
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556 {\bf Fonts.} |
519
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557 In order to have a good variety of sources we downloaded an important number of free fonts from: |
530
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558 {\tt http://cg.scs.carleton.ca/\textasciitilde luc/freefonts.html}. |
519
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559 % TODO: pointless to anonymize, it's not pointing to our work |
530
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560 Including the operating system's (Windows 7) fonts, there is a total of $9817$ different fonts that we can choose uniformly from. |
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561 The chosen {\tt ttf} file is either used as input of the Captcha generator (see next item) or, by producing a corresponding image, |
479
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562 directly as input to our models. |
549 | 563 \vspace*{-1mm} |
479
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564 |
484
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565 %\item |
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566 {\bf Captchas.} |
464
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parents:
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|
567 The Captcha data source is an adaptation of the \emph{pycaptcha} library (a python based captcha generator library) for |
472 | 568 generating characters of the same format as the NIST dataset. This software is based on |
495 | 569 a random character class generator and various kinds of transformations similar to those described in the previous sections. |
472 | 570 In order to increase the variability of the data generated, many different fonts are used for generating the characters. |
495 | 571 Transformations (slant, distortions, rotation, translation) are applied to each randomly generated character with a complexity |
519
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|
572 depending on the value of the complexity parameter provided by the user of the data source. |
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573 %Two levels of complexity are allowed and can be controlled via an easy to use facade class. %TODO: what's a facade class? |
549 | 574 \vspace*{-1mm} |
484
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575 |
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576 %\item |
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577 {\bf OCR data.} |
472 | 578 A large set (2 million) of scanned, OCRed and manually verified machine-printed |
553
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|
579 characters where included as an |
472 | 580 additional source. This set is part of a larger corpus being collected by the Image Understanding |
550
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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549
diff
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|
581 Pattern Recognition Research group led by Thomas Breuel at University of Kaiserslautern |
495 | 582 ({\tt http://www.iupr.com}), and which will be publicly released. |
519
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583 %TODO: let's hope that Thomas is not a reviewer! :) Seriously though, maybe we should anonymize this |
484
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584 %\end{itemize} |
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585 |
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586 \vspace*{-3mm} |
472 | 587 \subsection{Data Sets} |
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588 \vspace*{-2mm} |
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589 |
472 | 590 All data sets contain 32$\times$32 grey-level images (values in $[0,1]$) associated with a label |
591 from one of the 62 character classes. | |
484
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592 %\begin{itemize} |
549 | 593 \vspace*{-1mm} |
484
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594 |
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595 %\item |
534 | 596 {\bf NIST.} This is the raw NIST special database 19~\citep{Grother-1995}. It has |
535 | 597 \{651668 / 80000 / 82587\} \{training / validation / test\} examples. |
549 | 598 \vspace*{-1mm} |
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599 |
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600 %\item |
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601 {\bf P07.} This dataset is obtained by taking raw characters from all four of the above sources |
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602 and sending them through the transformation pipeline described in section \ref{s:perturbations}. |
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603 For each new example to generate, a data source is selected with probability $10\%$ from the fonts, |
472 | 604 $25\%$ from the captchas, $25\%$ from the OCR data and $40\%$ from NIST. We apply all the transformations in the |
530
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605 order given above, and for each of them we sample uniformly a \emph{complexity} in the range $[0,0.7]$. |
535 | 606 It has \{81920000 / 80000 / 20000\} \{training / validation / test\} examples. |
549 | 607 \vspace*{-1mm} |
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608 |
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609 %\item |
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610 {\bf NISTP.} This one is equivalent to P07 (complexity parameter of $0.7$ with the same proportions of data sources) |
464
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611 except that we only apply |
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parents:
diff
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|
612 transformations from slant to pinch. Therefore, the character is |
495 | 613 transformed but no additional noise is added to the image, giving images |
534 | 614 closer to the NIST dataset. |
535 | 615 It has \{81920000 / 80000 / 20000\} \{training / validation / test\} examples. |
484
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616 %\end{itemize} |
464
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617 |
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618 \vspace*{-3mm} |
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619 \subsection{Models and their Hyperparameters} |
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620 \vspace*{-2mm} |
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621 |
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622 The experiments are performed with Multi-Layer Perceptrons (MLP) with a single |
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623 hidden layer and with Stacked Denoising Auto-Encoders (SDA). |
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624 \emph{Hyper-parameters are selected based on the {\bf NISTP} validation set error.} |
472 | 625 |
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626 {\bf Multi-Layer Perceptrons (MLP).} |
472 | 627 Whereas previous work had compared deep architectures to both shallow MLPs and |
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628 SVMs, we only compared to MLPs here because of the very large datasets used |
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629 (making the use of SVMs computationally challenging because of their quadratic |
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630 scaling behavior). |
472 | 631 The MLP has a single hidden layer with $\tanh$ activation functions, and softmax (normalized |
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632 exponentials) on the output layer for estimating $P(class | image)$. |
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633 The number of hidden units is taken in $\{300,500,800,1000,1500\}$. |
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634 Training examples are presented in minibatches of size 20. A constant learning |
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635 rate was chosen among $\{0.001, 0.01, 0.025, 0.075, 0.1, 0.5\}$ |
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636 through preliminary experiments (measuring performance on a validation set), |
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637 and $0.1$ was then selected for optimizing on the whole training sets. |
549 | 638 \vspace*{-1mm} |
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639 |
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640 |
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641 {\bf Stacked Denoising Auto-Encoders (SDA).} |
472 | 642 Various auto-encoder variants and Restricted Boltzmann Machines (RBMs) |
643 can be used to initialize the weights of each layer of a deep MLP (with many hidden | |
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644 layers)~\citep{Hinton06,ranzato-07-small,Bengio-nips-2006}, |
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645 apparently setting parameters in the |
472 | 646 basin of attraction of supervised gradient descent yielding better |
647 generalization~\citep{Erhan+al-2010}. It is hypothesized that the | |
648 advantage brought by this procedure stems from a better prior, | |
649 on the one hand taking advantage of the link between the input | |
650 distribution $P(x)$ and the conditional distribution of interest | |
651 $P(y|x)$ (like in semi-supervised learning), and on the other hand | |
652 taking advantage of the expressive power and bias implicit in the | |
653 deep architecture (whereby complex concepts are expressed as | |
654 compositions of simpler ones through a deep hierarchy). | |
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655 |
547 | 656 \begin{figure}[ht] |
657 \vspace*{-2mm} | |
658 \centerline{\resizebox{0.8\textwidth}{!}{\includegraphics{images/denoising_autoencoder_small.pdf}}} | |
549 | 659 \vspace*{-2mm} |
547 | 660 \caption{Illustration of the computations and training criterion for the denoising |
661 auto-encoder used to pre-train each layer of the deep architecture. Input $x$ of | |
662 the layer (i.e. raw input or output of previous layer) | |
663 is corrupted into $\tilde{x}$ and encoded into code $y$ by the encoder $f_\theta(\cdot)$. | |
664 The decoder $g_{\theta'}(\cdot)$ maps $y$ to reconstruction $z$, which | |
665 is compared to the uncorrupted input $x$ through the loss function | |
666 $L_H(x,z)$, whose expected value is approximately minimized during training | |
667 by tuning $\theta$ and $\theta'$.} | |
668 \label{fig:da} | |
669 \vspace*{-2mm} | |
670 \end{figure} | |
671 | |
472 | 672 Here we chose to use the Denoising |
673 Auto-Encoder~\citep{VincentPLarochelleH2008} as the building block for | |
674 these deep hierarchies of features, as it is very simple to train and | |
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675 explain (see Figure~\ref{fig:da}, as well as |
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676 tutorial and code there: {\tt http://deeplearning.net/tutorial}), |
472 | 677 provides immediate and efficient inference, and yielded results |
678 comparable or better than RBMs in series of experiments | |
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679 \citep{VincentPLarochelleH2008}. During training, a Denoising |
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680 Auto-Encoder is presented with a stochastically corrupted version |
472 | 681 of the input and trained to reconstruct the uncorrupted input, |
682 forcing the hidden units to represent the leading regularities in | |
535 | 683 the data. Once it is trained, in a purely unsupervised way, |
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684 its hidden units' activations can |
472 | 685 be used as inputs for training a second one, etc. |
686 After this unsupervised pre-training stage, the parameters | |
687 are used to initialize a deep MLP, which is fine-tuned by | |
688 the same standard procedure used to train them (see previous section). | |
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689 The SDA hyper-parameters are the same as for the MLP, with the addition of the |
472 | 690 amount of corruption noise (we used the masking noise process, whereby a |
691 fixed proportion of the input values, randomly selected, are zeroed), and a | |
692 separate learning rate for the unsupervised pre-training stage (selected | |
693 from the same above set). The fraction of inputs corrupted was selected | |
694 among $\{10\%, 20\%, 50\%\}$. Another hyper-parameter is the number | |
695 of hidden layers but it was fixed to 3 based on previous work with | |
547 | 696 SDAs on MNIST~\citep{VincentPLarochelleH2008}. |
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697 |
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698 \vspace*{-1mm} |
523 | 699 |
700 \begin{figure}[ht] | |
541 | 701 \vspace*{-2mm} |
523 | 702 \centerline{\resizebox{.99\textwidth}{!}{\includegraphics{images/error_rates_charts.pdf}}} |
549 | 703 \vspace*{-3mm} |
547 | 704 \caption{SDAx are the {\bf deep} models. Error bars indicate a 95\% confidence interval. 0 indicates that the model was trained |
523 | 705 on NIST, 1 on NISTP, and 2 on P07. Left: overall results |
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706 of all models, on NIST and NISTP test sets. |
523 | 707 Right: error rates on NIST test digits only, along with the previous results from |
708 literature~\citep{Granger+al-2007,Cortes+al-2000,Oliveira+al-2002-short,Milgram+al-2005} | |
709 respectively based on ART, nearest neighbors, MLPs, and SVMs.} | |
710 \label{fig:error-rates-charts} | |
541 | 711 \vspace*{-2mm} |
523 | 712 \end{figure} |
713 | |
714 | |
557
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715 \begin{figure}[ht] |
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716 \vspace*{-3mm} |
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717 \centerline{\resizebox{.99\textwidth}{!}{\includegraphics{images/improvements_charts.pdf}}} |
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718 \vspace*{-3mm} |
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719 \caption{Relative improvement in error rate due to self-taught learning. |
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720 Left: Improvement (or loss, when negative) |
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721 induced by out-of-distribution examples (perturbed data). |
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722 Right: Improvement (or loss, when negative) induced by multi-task |
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723 learning (training on all classes and testing only on either digits, |
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724 upper case, or lower-case). The deep learner (SDA) benefits more from |
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725 both self-taught learning scenarios, compared to the shallow MLP.} |
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726 \label{fig:improvements-charts} |
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727 \vspace*{-2mm} |
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728 \end{figure} |
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729 |
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730 \section{Experimental Results} |
549 | 731 \vspace*{-2mm} |
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732 |
485
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733 %\vspace*{-1mm} |
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734 %\subsection{SDA vs MLP vs Humans} |
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735 %\vspace*{-1mm} |
535 | 736 The models are either trained on NIST (MLP0 and SDA0), |
737 NISTP (MLP1 and SDA1), or P07 (MLP2 and SDA2), and tested | |
550
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738 on either NIST, NISTP or P07, either on the 62-class task |
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739 or on the 10-digits task. |
485
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740 Figure~\ref{fig:error-rates-charts} summarizes the results obtained, |
550
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741 comparing humans, the three MLPs (MLP0, MLP1, MLP2) and the three SDAs (SDA0, SDA1, |
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742 SDA2), along with the previous results on the digits NIST special database |
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743 19 test set from the literature respectively based on ARTMAP neural |
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744 networks ~\citep{Granger+al-2007}, fast nearest-neighbor search |
516
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745 ~\citep{Cortes+al-2000}, MLPs ~\citep{Oliveira+al-2002-short}, and SVMs |
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746 ~\citep{Milgram+al-2005}. More detailed and complete numerical results |
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747 (figures and tables, including standard errors on the error rates) can be |
535 | 748 found in Appendix I of the supplementary material. |
749 The deep learner not only outperformed the shallow ones and | |
493
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750 previously published performance (in a statistically and qualitatively |
535 | 751 significant way) but when trained with perturbed data |
752 reaches human performance on both the 62-class task | |
523 | 753 and the 10-class (digits) task. |
550
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754 17\% error (SDA1) or 18\% error (humans) may seem large but a large |
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755 majority of the errors from humans and from SDA1 are from out-of-context |
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756 confusions (e.g. a vertical bar can be a ``1'', an ``l'' or an ``L'', and a |
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757 ``c'' and a ``C'' are often indistinguishible). |
523 | 758 |
759 In addition, as shown in the left of | |
760 Figure~\ref{fig:improvements-charts}, the relative improvement in error | |
493
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761 rate brought by self-taught learning is greater for the SDA, and these |
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762 differences with the MLP are statistically and qualitatively |
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763 significant. |
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764 The left side of the figure shows the improvement to the clean |
493
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765 NIST test set error brought by the use of out-of-distribution examples |
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766 (i.e. the perturbed examples examples from NISTP or P07). |
547 | 767 Relative percent change is measured by taking |
548
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768 $100 \% \times$ (original model's error / perturbed-data model's error - 1). |
502
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769 The right side of |
523 | 770 Figure~\ref{fig:improvements-charts} shows the relative improvement |
486
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771 brought by the use of a multi-task setting, in which the same model is |
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772 trained for more classes than the target classes of interest (i.e. training |
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773 with all 62 classes when the target classes are respectively the digits, |
493
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774 lower-case, or upper-case characters). Again, whereas the gain from the |
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775 multi-task setting is marginal or negative for the MLP, it is substantial |
547 | 776 for the SDA. Note that to simplify these multi-task experiments, only the original |
493
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777 NIST dataset is used. For example, the MLP-digits bar shows the relative |
547 | 778 percent improvement in MLP error rate on the NIST digits test set |
548
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779 is $100\% \times$ (1 - single-task |
493
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780 model's error / multi-task model's error). The single-task model is |
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781 trained with only 10 outputs (one per digit), seeing only digit examples, |
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782 whereas the multi-task model is trained with 62 outputs, with all 62 |
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783 character classes as examples. Hence the hidden units are shared across |
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784 all tasks. For the multi-task model, the digit error rate is measured by |
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785 comparing the correct digit class with the output class associated with the |
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786 maximum conditional probability among only the digit classes outputs. The |
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787 setting is similar for the other two target classes (lower case characters |
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788 and upper case characters). |
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789 %\vspace*{-1mm} |
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790 %\subsection{Perturbed Training Data More Helpful for SDA} |
485
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791 %\vspace*{-1mm} |
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792 |
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793 %\vspace*{-1mm} |
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794 %\subsection{Multi-Task Learning Effects} |
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795 %\vspace*{-1mm} |
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796 |
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797 \iffalse |
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798 As previously seen, the SDA is better able to benefit from the |
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799 transformations applied to the data than the MLP. In this experiment we |
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800 define three tasks: recognizing digits (knowing that the input is a digit), |
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801 recognizing upper case characters (knowing that the input is one), and |
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802 recognizing lower case characters (knowing that the input is one). We |
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803 consider the digit classification task as the target task and we want to |
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804 evaluate whether training with the other tasks can help or hurt, and |
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805 whether the effect is different for MLPs versus SDAs. The goal is to find |
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|
806 out if deep learning can benefit more (or less) from multiple related tasks |
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807 (i.e. the multi-task setting) compared to a corresponding purely supervised |
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808 shallow learner. |
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|
809 |
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|
810 We use a single hidden layer MLP with 1000 hidden units, and a SDA |
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|
811 with 3 hidden layers (1000 hidden units per layer), pre-trained and |
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812 fine-tuned on NIST. |
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813 |
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814 Our results show that the MLP benefits marginally from the multi-task setting |
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815 in the case of digits (5\% relative improvement) but is actually hurt in the case |
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816 of characters (respectively 3\% and 4\% worse for lower and upper class characters). |
495 | 817 On the other hand the SDA benefited from the multi-task setting, with relative |
464
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818 error rate improvements of 27\%, 15\% and 13\% respectively for digits, |
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|
819 lower and upper case characters, as shown in Table~\ref{tab:multi-task}. |
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820 \fi |
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|
821 |
475 | 822 |
549 | 823 \vspace*{-2mm} |
529 | 824 \section{Conclusions and Discussion} |
549 | 825 \vspace*{-2mm} |
464
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826 |
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827 We have found that the self-taught learning framework is more beneficial |
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828 to a deep learner than to a traditional shallow and purely |
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829 supervised learner. More precisely, |
520
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519
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|
830 the answers are positive for all the questions asked in the introduction. |
484
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831 %\begin{itemize} |
487 | 832 |
484
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833 $\bullet$ %\item |
547 | 834 {\bf Do the good results previously obtained with deep architectures on the |
549 | 835 MNIST digits generalize to a much larger and richer (but similar) |
547 | 836 dataset, the NIST special database 19, with 62 classes and around 800k examples}? |
502
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837 Yes, the SDA {\bf systematically outperformed the MLP and all the previously |
529 | 838 published results on this dataset} (the ones that we are aware of), {\bf in fact reaching human-level |
839 performance} at around 17\% error on the 62-class task and 1.4\% on the digits. | |
484
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840 |
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841 $\bullet$ %\item |
547 | 842 {\bf To what extent do self-taught learning scenarios help deep learners, |
843 and do they help them more than shallow supervised ones}? | |
529 | 844 We found that distorted training examples not only made the resulting |
845 classifier better on similarly perturbed images but also on | |
846 the {\em original clean examples}, and more importantly and more novel, | |
847 that deep architectures benefit more from such {\em out-of-distribution} | |
848 examples. MLPs were helped by perturbed training examples when tested on perturbed input | |
502
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849 images (65\% relative improvement on NISTP) |
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850 but only marginally helped (5\% relative improvement on all classes) |
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851 or even hurt (10\% relative loss on digits) |
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852 with respect to clean examples . On the other hand, the deep SDAs |
472 | 853 were very significantly boosted by these out-of-distribution examples. |
529 | 854 Similarly, whereas the improvement due to the multi-task setting was marginal or |
502
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|
855 negative for the MLP (from +5.6\% to -3.6\% relative change), |
547 | 856 it was very significant for the SDA (from +13\% to +27\% relative change), |
857 which may be explained by the arguments below. | |
484
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858 %\end{itemize} |
472 | 859 |
524
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860 In the original self-taught learning framework~\citep{RainaR2007}, the |
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861 out-of-sample examples were used as a source of unsupervised data, and |
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862 experiments showed its positive effects in a \emph{limited labeled data} |
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863 scenario. However, many of the results by \citet{RainaR2007} (who used a |
550
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864 shallow, sparse coding approach) suggest that the {\em relative gain of self-taught |
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865 learning vs ordinary supervised learning} diminishes as the number of labeled examples increases. |
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866 We note instead that, for deep |
524
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|
867 architectures, our experiments show that such a positive effect is accomplished |
550
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868 even in a scenario with a \emph{very large number of labeled examples}, |
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869 i.e., here, the relative gain of self-taught learning is probably preserved |
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|
870 in the asymptotic regime. |
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871 |
547 | 872 {\bf Why would deep learners benefit more from the self-taught learning framework}? |
502
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873 The key idea is that the lower layers of the predictor compute a hierarchy |
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|
874 of features that can be shared across tasks or across variants of the |
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|
875 input distribution. Intermediate features that can be used in different |
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|
876 contexts can be estimated in a way that allows to share statistical |
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|
877 strength. Features extracted through many levels are more likely to |
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|
878 be more abstract (as the experiments in~\citet{Goodfellow2009} suggest), |
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|
879 increasing the likelihood that they would be useful for a larger array |
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|
880 of tasks and input conditions. |
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881 Therefore, we hypothesize that both depth and unsupervised |
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882 pre-training play a part in explaining the advantages observed here, and future |
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|
883 experiments could attempt at teasing apart these factors. |
529 | 884 And why would deep learners benefit from the self-taught learning |
885 scenarios even when the number of labeled examples is very large? | |
886 We hypothesize that this is related to the hypotheses studied | |
887 in~\citet{Erhan+al-2010}. Whereas in~\citet{Erhan+al-2010} | |
888 it was found that online learning on a huge dataset did not make the | |
889 advantage of the deep learning bias vanish, a similar phenomenon | |
890 may be happening here. We hypothesize that unsupervised pre-training | |
891 of a deep hierarchy with self-taught learning initializes the | |
892 model in the basin of attraction of supervised gradient descent | |
893 that corresponds to better generalization. Furthermore, such good | |
894 basins of attraction are not discovered by pure supervised learning | |
895 (with or without self-taught settings), and more labeled examples | |
550
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diff
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|
896 does not allow the model to go from the poorer basins of attraction discovered |
529 | 897 by the purely supervised shallow models to the kind of better basins associated |
898 with deep learning and self-taught learning. | |
502
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899 |
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900 A Flash demo of the recognizer (where both the MLP and the SDA can be compared) |
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901 can be executed on-line at {\tt http://deep.host22.com}. |
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902 |
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903 \newpage |
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904 { |
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905 \bibliography{strings,strings-short,strings-shorter,ift6266_ml,aigaion-shorter,specials} |
469 | 906 %\bibliographystyle{plainnat} |
907 \bibliographystyle{unsrtnat} | |
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908 %\bibliographystyle{apalike} |
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909 } |
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910 |
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911 |
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912 \end{document} |