Mercurial > ift6266
annotate writeup/nips2010_submission.tex @ 555:b6dfba0a110c
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author | Yoshua Bengio <bengioy@iro.umontreal.ca> |
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date | Thu, 03 Jun 2010 08:09:35 -0400 |
parents | e95395f51d72 |
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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 |
110 for stochastically transforming character images. The conclusion discusses | |
111 the more general question of why deep learners may benefit so much from | |
112 the self-taught learning framework. | |
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113 |
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114 \vspace*{-1mm} |
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115 \section{Perturbation and Transformation of Character Images} |
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116 \label{s:perturbations} |
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117 \vspace*{-1mm} |
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118 |
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119 \begin{wrapfigure}[8]{l}{0.15\textwidth} |
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120 %\begin{minipage}[b]{0.14\linewidth} |
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121 \vspace*{-5mm} |
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122 \begin{center} |
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123 \includegraphics[scale=.4]{images/Original.PNG}\\ |
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124 {\bf Original} |
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125 \end{center} |
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126 \end{wrapfigure} |
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127 %\vspace{0.7cm} |
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128 %\end{minipage}% |
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129 %\hspace{0.3cm}\begin{minipage}[b]{0.86\linewidth} |
467 | 130 This section describes the different transformations we used to stochastically |
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131 transform source images such as the one on the left |
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132 in order to obtain data from a larger distribution which |
547 | 133 covers a domain substantially larger than the clean characters distribution from |
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134 which we start. |
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135 Although character transformations have been used before to |
547 | 136 improve character recognizers, this effort is on a large scale both |
137 in number of classes and in the complexity of the transformations, hence | |
138 in the complexity of the learning task. | |
139 More details can | |
469 | 140 be found in this technical report~\citep{ift6266-tr-anonymous}. |
464
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141 The code for these transformations (mostly python) is available at |
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142 {\tt http://anonymous.url.net}. All the modules in the pipeline share |
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143 a global control parameter ($0 \le complexity \le 1$) that allows one to modulate the |
467 | 144 amount of deformation or noise introduced. |
145 There are two main parts in the pipeline. The first one, | |
146 from slant to pinch below, performs transformations. The second | |
147 part, from blur to contrast, adds different kinds of noise. | |
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148 %\end{minipage} |
464
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149 |
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150 \vspace*{1mm} |
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151 %\subsection{Transformations} |
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152 {\large\bf 2.1 Transformations} |
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153 \vspace*{1mm} |
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154 |
501 | 155 |
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156 \begin{wrapfigure}[7]{l}{0.15\textwidth} |
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157 %\begin{minipage}[b]{0.14\linewidth} |
551
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158 %\centering |
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159 \begin{center} |
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160 \vspace*{-5mm} |
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161 \includegraphics[scale=.4]{images/Thick_only.PNG}\\ |
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162 {\bf Thickness} |
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163 \end{center} |
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164 %\vspace{.6cm} |
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165 %\end{minipage}% |
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166 %\hspace{0.3cm}\begin{minipage}[b]{0.86\linewidth} |
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167 \end{wrapfigure} |
495 | 168 Morphological operators of dilation and erosion~\citep{Haralick87,Serra82} |
467 | 169 are applied. The neighborhood of each pixel is multiplied |
170 element-wise with a {\em structuring element} matrix. | |
171 The pixel value is replaced by the maximum or the minimum of the resulting | |
172 matrix, respectively for dilation or erosion. Ten different structural elements with | |
173 increasing dimensions (largest is $5\times5$) were used. For each image, | |
174 randomly sample the operator type (dilation or erosion) with equal probability and one structural | |
541 | 175 element from a subset of the $n=round(m \times complexity)$ smallest structuring elements |
176 where $m=10$ for dilation and $m=6$ for erosion (to avoid completely erasing thin characters). | |
177 A neutral element (no transformation) | |
178 is always present in the set. is applied. | |
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179 %\vspace{.4cm} |
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180 %\end{minipage} |
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181 %\vspace{-.7cm} |
523 | 182 |
551
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183 \begin{minipage}[b]{0.14\linewidth} |
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184 \centering |
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185 \includegraphics[scale=.4]{images/Slant_only.PNG}\\ |
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186 {\bf Slant} |
551
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187 \end{minipage}% |
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188 \hspace{0.3cm}\begin{minipage}[b]{0.83\linewidth} |
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189 %\centering |
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190 %\vspace*{-15mm} |
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191 Each row of the image is shifted |
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192 proportionally to its height: $shift = round(slant \times height)$. |
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193 $slant \sim U[-complexity,complexity]$. |
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194 \vspace{1.5cm} |
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195 \end{minipage} |
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196 %\vspace*{-4mm} |
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197 |
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198 %\begin{minipage}[b]{0.14\linewidth} |
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199 %\centering |
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200 \begin{wrapfigure}[8]{l}{0.15\textwidth} |
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201 \vspace*{-6mm} |
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202 \begin{center} |
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203 \includegraphics[scale=.4]{images/Affine_only.PNG}\\ |
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204 {\bf Affine} |
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205 \end{center} |
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206 \end{wrapfigure} |
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207 %\end{minipage}% |
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208 %\hspace{0.3cm}\begin{minipage}[b]{0.86\linewidth} |
467 | 209 A $2 \times 3$ affine transform matrix (with |
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210 parameters $(a,b,c,d,e,f)$) is sampled according to the $complexity$. |
541 | 211 Output pixel $(x,y)$ takes the value of input pixel |
212 nearest to $(ax+by+c,dx+ey+f)$, | |
213 producing scaling, translation, rotation and shearing. | |
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214 Marginal distributions of $(a,b,c,d,e,f)$ have been tuned to |
550
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215 forbid large rotations (not to confuse classes) but to give good |
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216 variability of the transformation: $a$ and $d$ $\sim U[1-3 |
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217 complexity,1+3\,complexity]$, $b$ and $e$ $\sim[-3 \,complexity,3\, |
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218 complexity]$ and $c$ and $f$ $\sim U[-4 \,complexity, 4 \, |
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219 complexity]$.\\ |
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220 %\end{minipage} |
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221 |
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222 \vspace*{-4.5mm} |
523 | 223 |
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224 \begin{minipage}[t]{\linewidth} |
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225 \begin{wrapfigure}[7]{l}{0.15\textwidth} |
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226 %\hspace*{-8mm}\begin{minipage}[b]{0.25\linewidth} |
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227 %\centering |
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228 \begin{center} |
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229 \vspace*{-4mm} |
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230 \includegraphics[scale=.4]{images/Localelasticdistorsions_only.PNG}\\ |
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231 {\bf Local Elastic} |
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232 \end{center} |
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233 \end{wrapfigure} |
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234 %\end{minipage}% |
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235 %\hspace{-3mm}\begin{minipage}[b]{0.85\linewidth} |
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236 %\vspace*{-20mm} |
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237 This local elastic deformation |
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238 filter induces a ``wiggly'' effect in the image, following~\citet{SimardSP03-short}, |
467 | 239 which provides more details. |
541 | 240 The intensity of the displacement fields is given by |
241 $\alpha = \sqrt[3]{complexity} \times 10.0$, which are | |
242 convolved with a Gaussian 2D kernel (resulting in a blur) of | |
243 standard deviation $\sigma = 10 - 7 \times\sqrt[3]{complexity}$. | |
555
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244 %\vspace{.9cm} |
551
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245 \end{minipage} |
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246 |
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247 \vspace*{5mm} |
523 | 248 |
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249 %\begin{minipage}[b]{0.14\linewidth} |
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250 %\centering |
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251 \begin{wrapfigure}[7]{l}{0.15\textwidth} |
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252 \vspace*{-5mm} |
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253 \begin{center} |
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254 \includegraphics[scale=.4]{images/Pinch_only.PNG}\\ |
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255 {\bf Pinch} |
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256 \end{center} |
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257 \end{wrapfigure} |
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258 %\vspace{.6cm} |
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259 %\end{minipage}% |
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260 %\hspace{0.3cm}\begin{minipage}[b]{0.86\linewidth} |
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261 This is the ``Whirl and pinch'' GIMP filter with whirl was set to 0. |
541 | 262 A pinch is ``similar to projecting the image onto an elastic |
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263 surface and pressing or pulling on the center of the surface'' (GIMP documentation manual). |
555
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264 For a square input image, draw a radius-$r$ disk |
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265 around $C$. Any pixel $P$ belonging to |
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266 that disk has its value replaced by |
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267 the value of a ``source'' pixel in the original image, |
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268 on the line that goes through $C$ and $P$, but |
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269 at some other distance $d_2$. Define $d_1=distance(P,C) = sin(\frac{\pi{}d_1}{2r})^{-pinch} \times |
464
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270 d_1$, where $pinch$ is a parameter to the filter. |
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271 The actual value is given by bilinear interpolation considering the pixels |
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272 around the (non-integer) source position thus found. |
484
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273 Here $pinch \sim U[-complexity, 0.7 \times complexity]$. |
551
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274 %\vspace{1.5cm} |
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275 %\end{minipage} |
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276 |
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277 \vspace{2mm} |
484
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278 |
555
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279 {\large\bf 2.2 Injecting Noise} |
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280 %\subsection{Injecting Noise} |
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281 \vspace{2mm} |
484
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282 |
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283 %\vspace*{-.2cm} |
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284 \begin{minipage}[t]{0.14\linewidth} |
551
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285 \centering |
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286 \vspace*{-2mm} |
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287 \includegraphics[scale=.4]{images/Motionblur_only.PNG}\\ |
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288 {\bf Motion Blur} |
551
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289 \end{minipage}% |
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290 \hspace{0.3cm}\begin{minipage}[t]{0.83\linewidth} |
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291 %\vspace*{.5mm} |
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292 This is GIMP's ``linear motion blur'' |
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293 with parameters $length$ and $angle$. The value of |
555
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294 a pixel in the final image is approximately the mean of the first $length$ pixels |
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295 found by moving in the $angle$ direction, |
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296 $angle \sim U[0,360]$ degrees, and $length \sim {\rm Normal}(0,(3 \times complexity)^2)$. |
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297 \vspace{5mm} |
551
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298 \end{minipage} |
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299 |
555
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300 \vspace*{1mm} |
523 | 301 |
555
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302 \begin{minipage}[t]{0.14\linewidth} |
551
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303 \centering |
555
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304 \includegraphics[scale=.4]{images/occlusion_only.PNG}\\ |
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305 {\bf Occlusion} |
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306 %\vspace{.5cm} |
551
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307 \end{minipage}% |
555
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308 \hspace{0.3cm}\begin{minipage}[t]{0.83\linewidth} |
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309 \vspace*{-18mm} |
517
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310 Selects a random rectangle from an {\em occluder} character |
544
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311 image and places it over the original {\em occluded} |
467 | 312 image. Pixels are combined by taking the max(occluder,occluded), |
517
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313 closer to black. The rectangle corners |
467 | 314 are sampled so that larger complexity gives larger rectangles. |
315 The destination position in the occluded image are also sampled | |
544
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316 according to a normal distribution (more details in~\citet{ift6266-tr-anonymous}). |
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317 This filter is skipped with probability 60\%. |
555
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318 %\vspace{7mm} |
551
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319 \end{minipage} |
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320 |
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321 \vspace*{1mm} |
523 | 322 |
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323 \begin{wrapfigure}[8]{l}{0.15\textwidth} |
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324 \vspace*{-6mm} |
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325 \begin{center} |
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326 %\begin{minipage}[t]{0.14\linewidth} |
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327 %\centering |
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328 \includegraphics[scale=.4]{images/Bruitgauss_only.PNG}\\ |
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329 {\bf Gaussian Smoothing} |
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330 \end{center} |
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331 \end{wrapfigure} |
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332 %\vspace{.5cm} |
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333 %\end{minipage}% |
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334 %\hspace{0.3cm}\begin{minipage}[t]{0.86\linewidth} |
544
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335 Different regions of the image are spatially smoothed by convolving |
554 | 336 the image with a symmetric Gaussian kernel of |
495 | 337 size and variance chosen uniformly in the ranges $[12,12 + 20 \times |
464
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338 complexity]$ and $[2,2 + 6 \times complexity]$. The result is normalized |
554 | 339 between $0$ and $1$. We also create a symmetric weighted averaging window, of the |
464
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340 kernel size, with maximum value at the center. For each image we sample |
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341 uniformly from $3$ to $3 + 10 \times complexity$ pixels that will be |
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342 averaging centers between the original image and the filtered one. We |
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343 initialize to zero a mask matrix of the image size. For each selected pixel |
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344 we add to the mask the averaging window centered to it. The final image is |
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345 computed from the following element-wise operation: $\frac{image + filtered |
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346 image \times mask}{mask+1}$. |
544
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347 This filter is skipped with probability 75\%. |
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348 %\end{minipage} |
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349 |
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350 \newpage |
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351 |
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352 \vspace*{-9mm} |
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353 |
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354 %\hspace*{-3mm}\begin{minipage}[t]{0.18\linewidth} |
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355 %\centering |
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356 \begin{minipage}[t]{\linewidth} |
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357 \begin{wrapfigure}[7]{l}{0.15\textwidth} |
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358 \vspace*{-5mm} |
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359 \begin{center} |
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360 \includegraphics[scale=.4]{images/Permutpixel_only.PNG}\\ |
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361 {\small\bf Permute Pixels} |
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362 \end{center} |
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363 \end{wrapfigure} |
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364 %\end{minipage}% |
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365 %\hspace{-0cm}\begin{minipage}[t]{0.86\linewidth} |
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366 %\vspace*{-20mm} |
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367 This filter permutes neighbouring pixels. It first selects |
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368 fraction $\frac{complexity}{3}$ of pixels randomly in the image. Each of them are then |
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369 sequentially exchanged with one other in as $V4$ neighbourhood. |
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
370 This filter is skipped with probability 80\%.\\ |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
371 \vspace*{1mm} |
551
8f365abf171d
separete the transmo image
Frederic Bastien <nouiz@nouiz.org>
parents:
550
diff
changeset
|
372 \end{minipage} |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
373 |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
374 \vspace{-1mm} |
523 | 375 |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
376 \begin{minipage}[t]{\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
377 \begin{wrapfigure}[7]{l}{0.15\textwidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
378 %\vspace*{-3mm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
379 \begin{center} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
380 %\hspace*{-3mm}\begin{minipage}[t]{0.18\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
381 %\centering |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
382 \vspace*{-5mm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
383 \includegraphics[scale=.4]{images/Distorsiongauss_only.PNG}\\ |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
384 {\small \bf Gauss. Noise} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
385 \end{center} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
386 \end{wrapfigure} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
387 %\end{minipage}% |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
388 %\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
|
389 \vspace*{12mm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
390 This filter simply adds, to each pixel of the image independently, a |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
391 noise $\sim Normal(0,(\frac{complexity}{10})^2)$. |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
392 This filter is skipped with probability 70\%. |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
393 %\vspace{1.1cm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
394 \end{minipage} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
395 |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
396 \vspace*{1.5cm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
397 |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
398 \begin{minipage}[t]{\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
399 \begin{minipage}[t]{0.14\linewidth} |
551
8f365abf171d
separete the transmo image
Frederic Bastien <nouiz@nouiz.org>
parents:
550
diff
changeset
|
400 \centering |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
401 \includegraphics[scale=.4]{images/background_other_only.png}\\ |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
402 {\small \bf Bg Image} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
403 \end{minipage}% |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
404 \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
|
405 \vspace*{-18mm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
406 Following~\citet{Larochelle-jmlr-2009}, this transformation adds a random |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
407 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
|
408 with contrast adjustments depending on $complexity$, to preserve |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
409 more or less of the original character image. |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
410 %\vspace{.8cm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
411 \end{minipage} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
412 \end{minipage} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
413 %\vspace{-.7cm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
414 |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
415 \begin{minipage}[t]{0.14\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
416 \centering |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
417 \includegraphics[scale=.4]{images/Poivresel_only.PNG}\\ |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
418 {\small \bf Salt \& Pepper} |
551
8f365abf171d
separete the transmo image
Frederic Bastien <nouiz@nouiz.org>
parents:
550
diff
changeset
|
419 \end{minipage}% |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
420 \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
|
421 \vspace*{-18mm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
422 This filter adds noise $\sim U[0,1]$ to random subsets of pixels. |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
423 The number of selected pixels is $0.2 \times complexity$. |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
424 This filter is skipped with probability 75\%. |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
425 %\vspace{.9cm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
426 \end{minipage} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
427 %\vspace{-.7cm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
428 |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
429 \vspace{1mm} |
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 \begin{minipage}[t]{\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
432 \begin{wrapfigure}[7]{l}{0.14\textwidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
433 %\begin{minipage}[t]{0.14\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
434 %\centering |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
435 \begin{center} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
436 \vspace*{-4mm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
437 \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
|
438 {\bf Scratches} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
439 %\end{minipage}% |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
440 \end{center} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
441 \end{wrapfigure} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
442 %\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
|
443 %\vspace{.4cm} |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
444 The 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
|
445 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
|
446 at random among 500 such 1 images, |
467 | 447 randomly cropped and rotated by an angle $\sim Normal(0,(100 \times |
554 | 448 complexity)^2$ (in degrees), using bi-cubic interpolation. |
495 | 449 Two passes of a grey-scale morphological erosion filter |
467 | 450 are applied, reducing the width of the line |
451 by an amount controlled by $complexity$. | |
544
1cdfc17e890f
ca fitte maintenant
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
541
diff
changeset
|
452 This filter is skipped with probability 85\%. The probabilities |
1cdfc17e890f
ca fitte maintenant
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
541
diff
changeset
|
453 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
|
454 \end{minipage} |
523 | 455 |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
456 \vspace*{2mm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
457 |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
458 \begin{minipage}[t]{0.20\linewidth} |
551
8f365abf171d
separete the transmo image
Frederic Bastien <nouiz@nouiz.org>
parents:
550
diff
changeset
|
459 \centering |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
460 \hspace*{-7mm}\includegraphics[scale=.4]{images/Contrast_only.PNG}\\ |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
461 {\bf Grey \& Contrast} |
551
8f365abf171d
separete the transmo image
Frederic Bastien <nouiz@nouiz.org>
parents:
550
diff
changeset
|
462 \end{minipage}% |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
463 \hspace{-4mm}\begin{minipage}[t]{0.82\linewidth} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
464 \vspace*{-18mm} |
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
465 This filter 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
|
466 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
|
467 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
|
468 polarity is inverted with probability 50\%. |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
469 %\vspace{.7cm} |
551
8f365abf171d
separete the transmo image
Frederic Bastien <nouiz@nouiz.org>
parents:
550
diff
changeset
|
470 \end{minipage} |
555
b6dfba0a110c
ameliorer l'aspect visuel, Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
554
diff
changeset
|
471 \vspace{2mm} |
551
8f365abf171d
separete the transmo image
Frederic Bastien <nouiz@nouiz.org>
parents:
550
diff
changeset
|
472 |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
473 |
499
2b58eda9fc08
changements de Myriam
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
495
diff
changeset
|
474 \iffalse |
523 | 475 \begin{figure}[ht] |
538 | 476 \centerline{\resizebox{.9\textwidth}{!}{\includegraphics{images/example_t.png}}}\\ |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
477 \caption{Illustration of the pipeline of stochastic |
519
eaa595ea2402
section 3 quickpass
Dumitru Erhan <dumitru.erhan@gmail.com>
parents:
518
diff
changeset
|
478 transformations applied to the image of a lower-case \emph{t} |
464
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
479 (the upper left image). Each image in the pipeline (going from |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
480 left to right, first top line, then bottom line) shows the result |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
481 of applying one of the modules in the pipeline. The last image |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
482 (bottom right) is used as training example.} |
24f4a8b53fcc
nips2010_submission.tex
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
diff
changeset
|
483 \label{fig:pipeline} |
24f4a8b53fcc
nips2010_submission.tex
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484 \end{figure} |
499
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485 \fi |
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486 |
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487 |
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488 \vspace*{-2mm} |
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489 \section{Experimental Setup} |
484
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490 \vspace*{-1mm} |
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491 |
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492 Much previous work on deep learning had been performed on |
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493 the MNIST digits task~\citep{Hinton06,ranzato-07-small,Bengio-nips-2006,Salakhutdinov+Hinton-2009}, |
472 | 494 with 60~000 examples, and variants involving 10~000 |
553
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495 examples~\citep{Larochelle-jmlr-toappear-2008,VincentPLarochelleH2008}. |
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496 The focus here is on much larger training sets, from 10 times to |
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497 to 1000 times larger, and 62 classes. |
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498 |
535 | 499 The first step in constructing the larger datasets (called NISTP and P07) is to sample from |
499
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500 a {\em data source}: {\bf NIST} (NIST database 19), {\bf Fonts}, {\bf Captchas}, |
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501 and {\bf OCR data} (scanned machine printed characters). Once a character |
547 | 502 is sampled from one of these sources (chosen randomly), the second step is to |
503 apply a pipeline of transformations and/or noise processes described in section \ref{s:perturbations}. | |
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504 |
547 | 505 To provide a baseline of error rate comparison we also estimate human performance |
506 on both the 62-class task and the 10-class digits task. | |
535 | 507 We compare the best MLPs against |
508 the best SDAs (both models' hyper-parameters are selected to minimize the validation set error), | |
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509 along with a comparison against a precise estimate |
502
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510 of human performance obtained via Amazon's Mechanical Turk (AMT) |
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511 service (http://mturk.com). |
502
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512 AMT users are paid small amounts |
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513 of money to perform tasks for which human intelligence is required. |
522
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514 Mechanical Turk has been used extensively in natural language processing and vision. |
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515 %processing \citep{SnowEtAl2008} and vision |
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516 %\citep{SorokinAndForsyth2008,whitehill09}. |
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517 AMT users were presented |
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518 with 10 character images (from a test set) and asked to choose 10 corresponding ASCII |
502
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519 characters. They were forced to make a hard choice among the |
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520 62 or 10 character classes (all classes or digits only). |
553
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521 80 subjects classified 2500 images per (dataset,task) pair, |
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522 with the guarantee that 3 different subjects classified each image, allowing |
550
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523 us to estimate inter-human variability (e.g a standard error of 0.1\% |
553
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524 on the average 18.2\% error done by humans on the 62-class task NIST test set). |
502
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525 |
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526 \vspace*{-3mm} |
472 | 527 \subsection{Data Sources} |
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528 \vspace*{-2mm} |
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529 |
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530 %\begin{itemize} |
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531 %\item |
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532 {\bf NIST.} |
501 | 533 Our main source of characters is the NIST Special Database 19~\citep{Grother-1995}, |
472 | 534 widely used for training and testing character |
516
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535 recognition systems~\citep{Granger+al-2007,Cortes+al-2000,Oliveira+al-2002-short,Milgram+al-2005}. |
519
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536 The dataset is composed of 814255 digits and characters (upper and lower cases), with hand checked classifications, |
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537 extracted from handwritten sample forms of 3600 writers. The characters are labelled by one of the 62 classes |
519
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538 corresponding to ``0''-``9'',``A''-``Z'' and ``a''-``z''. The dataset contains 8 parts (partitions) of varying complexity. |
534 | 539 The fourth partition (called $hsf_4$, 82587 examples), |
540 experimentally recognized to be the most difficult one, is the one recommended | |
519
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541 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 | 542 for that purpose. We randomly split the remainder (731668 examples) into a training set and a validation set for |
543 model selection. | |
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544 The performances reported by previous work on that dataset mostly use only the digits. |
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545 Here we use all the classes both in the training and testing phase. This is especially |
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546 useful to estimate the effect of a multi-task setting. |
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547 The distribution of the classes in the NIST training and test sets differs |
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548 substantially, with relatively many more digits in the test set, and a more uniform distribution |
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549 of letters in the test set (where the letters are distributed |
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550 more like in natural text). |
549 | 551 \vspace*{-1mm} |
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552 |
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553 %\item |
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554 {\bf Fonts.} |
519
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555 In order to have a good variety of sources we downloaded an important number of free fonts from: |
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556 {\tt http://cg.scs.carleton.ca/\textasciitilde luc/freefonts.html}. |
519
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557 % TODO: pointless to anonymize, it's not pointing to our work |
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558 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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559 The chosen {\tt ttf} file is either used as input of the Captcha generator (see next item) or, by producing a corresponding image, |
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560 directly as input to our models. |
549 | 561 \vspace*{-1mm} |
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562 |
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563 %\item |
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564 {\bf Captchas.} |
464
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565 The Captcha data source is an adaptation of the \emph{pycaptcha} library (a python based captcha generator library) for |
472 | 566 generating characters of the same format as the NIST dataset. This software is based on |
495 | 567 a random character class generator and various kinds of transformations similar to those described in the previous sections. |
472 | 568 In order to increase the variability of the data generated, many different fonts are used for generating the characters. |
495 | 569 Transformations (slant, distortions, rotation, translation) are applied to each randomly generated character with a complexity |
519
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570 depending on the value of the complexity parameter provided by the user of the data source. |
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571 %Two levels of complexity are allowed and can be controlled via an easy to use facade class. %TODO: what's a facade class? |
549 | 572 \vspace*{-1mm} |
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573 |
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574 %\item |
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575 {\bf OCR data.} |
472 | 576 A large set (2 million) of scanned, OCRed and manually verified machine-printed |
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577 characters where included as an |
472 | 578 additional source. This set is part of a larger corpus being collected by the Image Understanding |
550
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579 Pattern Recognition Research group led by Thomas Breuel at University of Kaiserslautern |
495 | 580 ({\tt http://www.iupr.com}), and which will be publicly released. |
519
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581 %TODO: let's hope that Thomas is not a reviewer! :) Seriously though, maybe we should anonymize this |
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582 %\end{itemize} |
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583 |
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584 \vspace*{-3mm} |
472 | 585 \subsection{Data Sets} |
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586 \vspace*{-2mm} |
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587 |
472 | 588 All data sets contain 32$\times$32 grey-level images (values in $[0,1]$) associated with a label |
589 from one of the 62 character classes. | |
484
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590 %\begin{itemize} |
549 | 591 \vspace*{-1mm} |
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592 |
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593 %\item |
534 | 594 {\bf NIST.} This is the raw NIST special database 19~\citep{Grother-1995}. It has |
535 | 595 \{651668 / 80000 / 82587\} \{training / validation / test\} examples. |
549 | 596 \vspace*{-1mm} |
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597 |
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598 %\item |
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599 {\bf P07.} This dataset is obtained by taking raw characters from all four of the above sources |
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600 and sending them through the transformation pipeline described in section \ref{s:perturbations}. |
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601 For each new example to generate, a data source is selected with probability $10\%$ from the fonts, |
472 | 602 $25\%$ from the captchas, $25\%$ from the OCR data and $40\%$ from NIST. We apply all the transformations in the |
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603 order given above, and for each of them we sample uniformly a \emph{complexity} in the range $[0,0.7]$. |
535 | 604 It has \{81920000 / 80000 / 20000\} \{training / validation / test\} examples. |
549 | 605 \vspace*{-1mm} |
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606 |
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607 %\item |
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608 {\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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609 except that we only apply |
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610 transformations from slant to pinch. Therefore, the character is |
495 | 611 transformed but no additional noise is added to the image, giving images |
534 | 612 closer to the NIST dataset. |
535 | 613 It has \{81920000 / 80000 / 20000\} \{training / validation / test\} examples. |
484
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614 %\end{itemize} |
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615 |
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616 \vspace*{-3mm} |
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617 \subsection{Models and their Hyperparameters} |
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618 \vspace*{-2mm} |
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619 |
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620 The experiments are performed with Multi-Layer Perceptrons (MLP) with a single |
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621 hidden layer and with Stacked Denoising Auto-Encoders (SDA). |
553
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622 \emph{Hyper-parameters are selected based on the {\bf NISTP} validation set error.} |
472 | 623 |
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624 {\bf Multi-Layer Perceptrons (MLP).} |
472 | 625 Whereas previous work had compared deep architectures to both shallow MLPs and |
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626 SVMs, we only compared to MLPs here because of the very large datasets used |
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627 (making the use of SVMs computationally challenging because of their quadratic |
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628 scaling behavior). |
472 | 629 The MLP has a single hidden layer with $\tanh$ activation functions, and softmax (normalized |
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630 exponentials) on the output layer for estimating $P(class | image)$. |
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631 The number of hidden units is taken in $\{300,500,800,1000,1500\}$. |
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632 Training examples are presented in minibatches of size 20. A constant learning |
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633 rate was chosen among $\{0.001, 0.01, 0.025, 0.075, 0.1, 0.5\}$ |
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634 through preliminary experiments (measuring performance on a validation set), |
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635 and $0.1$ was then selected for optimizing on the whole training sets. |
549 | 636 \vspace*{-1mm} |
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637 |
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638 |
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639 {\bf Stacked Denoising Auto-Encoders (SDA).} |
472 | 640 Various auto-encoder variants and Restricted Boltzmann Machines (RBMs) |
641 can be used to initialize the weights of each layer of a deep MLP (with many hidden | |
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642 layers)~\citep{Hinton06,ranzato-07-small,Bengio-nips-2006}, |
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643 apparently setting parameters in the |
472 | 644 basin of attraction of supervised gradient descent yielding better |
645 generalization~\citep{Erhan+al-2010}. It is hypothesized that the | |
646 advantage brought by this procedure stems from a better prior, | |
647 on the one hand taking advantage of the link between the input | |
648 distribution $P(x)$ and the conditional distribution of interest | |
649 $P(y|x)$ (like in semi-supervised learning), and on the other hand | |
650 taking advantage of the expressive power and bias implicit in the | |
651 deep architecture (whereby complex concepts are expressed as | |
652 compositions of simpler ones through a deep hierarchy). | |
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653 |
547 | 654 \begin{figure}[ht] |
655 \vspace*{-2mm} | |
656 \centerline{\resizebox{0.8\textwidth}{!}{\includegraphics{images/denoising_autoencoder_small.pdf}}} | |
549 | 657 \vspace*{-2mm} |
547 | 658 \caption{Illustration of the computations and training criterion for the denoising |
659 auto-encoder used to pre-train each layer of the deep architecture. Input $x$ of | |
660 the layer (i.e. raw input or output of previous layer) | |
661 is corrupted into $\tilde{x}$ and encoded into code $y$ by the encoder $f_\theta(\cdot)$. | |
662 The decoder $g_{\theta'}(\cdot)$ maps $y$ to reconstruction $z$, which | |
663 is compared to the uncorrupted input $x$ through the loss function | |
664 $L_H(x,z)$, whose expected value is approximately minimized during training | |
665 by tuning $\theta$ and $\theta'$.} | |
666 \label{fig:da} | |
667 \vspace*{-2mm} | |
668 \end{figure} | |
669 | |
472 | 670 Here we chose to use the Denoising |
671 Auto-Encoder~\citep{VincentPLarochelleH2008} as the building block for | |
672 these deep hierarchies of features, as it is very simple to train and | |
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673 explain (see Figure~\ref{fig:da}, as well as |
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674 tutorial and code there: {\tt http://deeplearning.net/tutorial}), |
472 | 675 provides immediate and efficient inference, and yielded results |
676 comparable or better than RBMs in series of experiments | |
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677 \citep{VincentPLarochelleH2008}. During training, a Denoising |
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678 Auto-Encoder is presented with a stochastically corrupted version |
472 | 679 of the input and trained to reconstruct the uncorrupted input, |
680 forcing the hidden units to represent the leading regularities in | |
535 | 681 the data. Once it is trained, in a purely unsupervised way, |
550
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|
682 its hidden units' activations can |
472 | 683 be used as inputs for training a second one, etc. |
684 After this unsupervised pre-training stage, the parameters | |
685 are used to initialize a deep MLP, which is fine-tuned by | |
686 the same standard procedure used to train them (see previous section). | |
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687 The SDA hyper-parameters are the same as for the MLP, with the addition of the |
472 | 688 amount of corruption noise (we used the masking noise process, whereby a |
689 fixed proportion of the input values, randomly selected, are zeroed), and a | |
690 separate learning rate for the unsupervised pre-training stage (selected | |
691 from the same above set). The fraction of inputs corrupted was selected | |
692 among $\{10\%, 20\%, 50\%\}$. Another hyper-parameter is the number | |
693 of hidden layers but it was fixed to 3 based on previous work with | |
547 | 694 SDAs on MNIST~\citep{VincentPLarochelleH2008}. |
464
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695 |
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696 \vspace*{-1mm} |
523 | 697 |
698 \begin{figure}[ht] | |
541 | 699 \vspace*{-2mm} |
523 | 700 \centerline{\resizebox{.99\textwidth}{!}{\includegraphics{images/error_rates_charts.pdf}}} |
549 | 701 \vspace*{-3mm} |
547 | 702 \caption{SDAx are the {\bf deep} models. Error bars indicate a 95\% confidence interval. 0 indicates that the model was trained |
523 | 703 on NIST, 1 on NISTP, and 2 on P07. Left: overall results |
548
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704 of all models, on NIST and NISTP test sets. |
523 | 705 Right: error rates on NIST test digits only, along with the previous results from |
706 literature~\citep{Granger+al-2007,Cortes+al-2000,Oliveira+al-2002-short,Milgram+al-2005} | |
707 respectively based on ART, nearest neighbors, MLPs, and SVMs.} | |
708 \label{fig:error-rates-charts} | |
541 | 709 \vspace*{-2mm} |
523 | 710 \end{figure} |
711 | |
712 | |
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713 \section{Experimental Results} |
549 | 714 \vspace*{-2mm} |
464
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715 |
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716 %\vspace*{-1mm} |
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717 %\subsection{SDA vs MLP vs Humans} |
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718 %\vspace*{-1mm} |
535 | 719 The models are either trained on NIST (MLP0 and SDA0), |
720 NISTP (MLP1 and SDA1), or P07 (MLP2 and SDA2), and tested | |
550
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|
721 on either NIST, NISTP or P07, either on the 62-class task |
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722 or on the 10-digits task. |
485
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723 Figure~\ref{fig:error-rates-charts} summarizes the results obtained, |
550
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|
724 comparing humans, the three MLPs (MLP0, MLP1, MLP2) and the three SDAs (SDA0, SDA1, |
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725 SDA2), along with the previous results on the digits NIST special database |
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726 19 test set from the literature respectively based on ARTMAP neural |
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727 networks ~\citep{Granger+al-2007}, fast nearest-neighbor search |
516
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Frederic Bastien <nouiz@nouiz.org>
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diff
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|
728 ~\citep{Cortes+al-2000}, MLPs ~\citep{Oliveira+al-2002-short}, and SVMs |
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729 ~\citep{Milgram+al-2005}. More detailed and complete numerical results |
493
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730 (figures and tables, including standard errors on the error rates) can be |
535 | 731 found in Appendix I of the supplementary material. |
732 The deep learner not only outperformed the shallow ones and | |
493
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733 previously published performance (in a statistically and qualitatively |
535 | 734 significant way) but when trained with perturbed data |
735 reaches human performance on both the 62-class task | |
523 | 736 and the 10-class (digits) task. |
550
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737 17\% error (SDA1) or 18\% error (humans) may seem large but a large |
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|
738 majority of the errors from humans and from SDA1 are from out-of-context |
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|
739 confusions (e.g. a vertical bar can be a ``1'', an ``l'' or an ``L'', and a |
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740 ``c'' and a ``C'' are often indistinguishible). |
523 | 741 |
742 \begin{figure}[ht] | |
549 | 743 \vspace*{-3mm} |
523 | 744 \centerline{\resizebox{.99\textwidth}{!}{\includegraphics{images/improvements_charts.pdf}}} |
549 | 745 \vspace*{-3mm} |
523 | 746 \caption{Relative improvement in error rate due to self-taught learning. |
747 Left: Improvement (or loss, when negative) | |
748 induced by out-of-distribution examples (perturbed data). | |
749 Right: Improvement (or loss, when negative) induced by multi-task | |
750 learning (training on all classes and testing only on either digits, | |
751 upper case, or lower-case). The deep learner (SDA) benefits more from | |
752 both self-taught learning scenarios, compared to the shallow MLP.} | |
753 \label{fig:improvements-charts} | |
754 \vspace*{-2mm} | |
755 \end{figure} | |
756 | |
757 In addition, as shown in the left of | |
758 Figure~\ref{fig:improvements-charts}, the relative improvement in error | |
493
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759 rate brought by self-taught learning is greater for the SDA, and these |
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760 differences with the MLP are statistically and qualitatively |
502
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|
761 significant. |
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|
762 The left side of the figure shows the improvement to the clean |
493
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|
763 NIST test set error brought by the use of out-of-distribution examples |
502
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764 (i.e. the perturbed examples examples from NISTP or P07). |
547 | 765 Relative percent change is measured by taking |
548
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|
766 $100 \% \times$ (original model's error / perturbed-data model's error - 1). |
502
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|
767 The right side of |
523 | 768 Figure~\ref{fig:improvements-charts} shows the relative improvement |
486
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|
769 brought by the use of a multi-task setting, in which the same model is |
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|
770 trained for more classes than the target classes of interest (i.e. training |
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771 with all 62 classes when the target classes are respectively the digits, |
493
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772 lower-case, or upper-case characters). Again, whereas the gain from the |
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773 multi-task setting is marginal or negative for the MLP, it is substantial |
547 | 774 for the SDA. Note that to simplify these multi-task experiments, only the original |
493
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775 NIST dataset is used. For example, the MLP-digits bar shows the relative |
547 | 776 percent improvement in MLP error rate on the NIST digits test set |
548
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|
777 is $100\% \times$ (1 - single-task |
493
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|
778 model's error / multi-task model's error). The single-task model is |
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diff
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|
779 trained with only 10 outputs (one per digit), seeing only digit examples, |
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780 whereas the multi-task model is trained with 62 outputs, with all 62 |
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781 character classes as examples. Hence the hidden units are shared across |
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782 all tasks. For the multi-task model, the digit error rate is measured by |
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783 comparing the correct digit class with the output class associated with the |
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784 maximum conditional probability among only the digit classes outputs. The |
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785 setting is similar for the other two target classes (lower case characters |
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786 and upper case characters). |
485
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787 %\vspace*{-1mm} |
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788 %\subsection{Perturbed Training Data More Helpful for SDA} |
485
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789 %\vspace*{-1mm} |
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790 |
485
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791 %\vspace*{-1mm} |
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792 %\subsection{Multi-Task Learning Effects} |
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793 %\vspace*{-1mm} |
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794 |
485
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795 \iffalse |
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796 As previously seen, the SDA is better able to benefit from the |
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797 transformations applied to the data than the MLP. In this experiment we |
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798 define three tasks: recognizing digits (knowing that the input is a digit), |
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799 recognizing upper case characters (knowing that the input is one), and |
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800 recognizing lower case characters (knowing that the input is one). We |
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nips2010_submission.tex
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801 consider the digit classification task as the target task and we want to |
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802 evaluate whether training with the other tasks can help or hurt, and |
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803 whether the effect is different for MLPs versus SDAs. The goal is to find |
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804 out if deep learning can benefit more (or less) from multiple related tasks |
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805 (i.e. the multi-task setting) compared to a corresponding purely supervised |
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806 shallow learner. |
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807 |
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808 We use a single hidden layer MLP with 1000 hidden units, and a SDA |
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809 with 3 hidden layers (1000 hidden units per layer), pre-trained and |
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810 fine-tuned on NIST. |
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811 |
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812 Our results show that the MLP benefits marginally from the multi-task setting |
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813 in the case of digits (5\% relative improvement) but is actually hurt in the case |
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814 of characters (respectively 3\% and 4\% worse for lower and upper class characters). |
495 | 815 On the other hand the SDA benefited from the multi-task setting, with relative |
464
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816 error rate improvements of 27\%, 15\% and 13\% respectively for digits, |
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817 lower and upper case characters, as shown in Table~\ref{tab:multi-task}. |
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818 \fi |
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819 |
475 | 820 |
549 | 821 \vspace*{-2mm} |
529 | 822 \section{Conclusions and Discussion} |
549 | 823 \vspace*{-2mm} |
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824 |
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825 We have found that the self-taught learning framework is more beneficial |
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826 to a deep learner than to a traditional shallow and purely |
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827 supervised learner. More precisely, |
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828 the answers are positive for all the questions asked in the introduction. |
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829 %\begin{itemize} |
487 | 830 |
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831 $\bullet$ %\item |
547 | 832 {\bf Do the good results previously obtained with deep architectures on the |
549 | 833 MNIST digits generalize to a much larger and richer (but similar) |
547 | 834 dataset, the NIST special database 19, with 62 classes and around 800k examples}? |
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835 Yes, the SDA {\bf systematically outperformed the MLP and all the previously |
529 | 836 published results on this dataset} (the ones that we are aware of), {\bf in fact reaching human-level |
837 performance} at around 17\% error on the 62-class task and 1.4\% on the digits. | |
484
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838 |
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839 $\bullet$ %\item |
547 | 840 {\bf To what extent do self-taught learning scenarios help deep learners, |
841 and do they help them more than shallow supervised ones}? | |
529 | 842 We found that distorted training examples not only made the resulting |
843 classifier better on similarly perturbed images but also on | |
844 the {\em original clean examples}, and more importantly and more novel, | |
845 that deep architectures benefit more from such {\em out-of-distribution} | |
846 examples. MLPs were helped by perturbed training examples when tested on perturbed input | |
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847 images (65\% relative improvement on NISTP) |
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848 but only marginally helped (5\% relative improvement on all classes) |
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849 or even hurt (10\% relative loss on digits) |
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850 with respect to clean examples . On the other hand, the deep SDAs |
472 | 851 were very significantly boosted by these out-of-distribution examples. |
529 | 852 Similarly, whereas the improvement due to the multi-task setting was marginal or |
502
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853 negative for the MLP (from +5.6\% to -3.6\% relative change), |
547 | 854 it was very significant for the SDA (from +13\% to +27\% relative change), |
855 which may be explained by the arguments below. | |
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856 %\end{itemize} |
472 | 857 |
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858 In the original self-taught learning framework~\citep{RainaR2007}, the |
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859 out-of-sample examples were used as a source of unsupervised data, and |
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860 experiments showed its positive effects in a \emph{limited labeled data} |
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861 scenario. However, many of the results by \citet{RainaR2007} (who used a |
550
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862 shallow, sparse coding approach) suggest that the {\em relative gain of self-taught |
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863 learning vs ordinary supervised learning} diminishes as the number of labeled examples increases. |
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864 We note instead that, for deep |
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865 architectures, our experiments show that such a positive effect is accomplished |
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866 even in a scenario with a \emph{very large number of labeled examples}, |
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867 i.e., here, the relative gain of self-taught learning is probably preserved |
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868 in the asymptotic regime. |
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869 |
547 | 870 {\bf Why would deep learners benefit more from the self-taught learning framework}? |
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871 The key idea is that the lower layers of the predictor compute a hierarchy |
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872 of features that can be shared across tasks or across variants of the |
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873 input distribution. Intermediate features that can be used in different |
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874 contexts can be estimated in a way that allows to share statistical |
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875 strength. Features extracted through many levels are more likely to |
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876 be more abstract (as the experiments in~\citet{Goodfellow2009} suggest), |
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877 increasing the likelihood that they would be useful for a larger array |
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878 of tasks and input conditions. |
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879 Therefore, we hypothesize that both depth and unsupervised |
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880 pre-training play a part in explaining the advantages observed here, and future |
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881 experiments could attempt at teasing apart these factors. |
529 | 882 And why would deep learners benefit from the self-taught learning |
883 scenarios even when the number of labeled examples is very large? | |
884 We hypothesize that this is related to the hypotheses studied | |
885 in~\citet{Erhan+al-2010}. Whereas in~\citet{Erhan+al-2010} | |
886 it was found that online learning on a huge dataset did not make the | |
887 advantage of the deep learning bias vanish, a similar phenomenon | |
888 may be happening here. We hypothesize that unsupervised pre-training | |
889 of a deep hierarchy with self-taught learning initializes the | |
890 model in the basin of attraction of supervised gradient descent | |
891 that corresponds to better generalization. Furthermore, such good | |
892 basins of attraction are not discovered by pure supervised learning | |
893 (with or without self-taught settings), and more labeled examples | |
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894 does not allow the model to go from the poorer basins of attraction discovered |
529 | 895 by the purely supervised shallow models to the kind of better basins associated |
896 with deep learning and self-taught learning. | |
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897 |
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898 A Flash demo of the recognizer (where both the MLP and the SDA can be compared) |
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899 can be executed on-line at {\tt http://deep.host22.com}. |
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900 |
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901 \newpage |
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902 { |
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903 \bibliography{strings,strings-short,strings-shorter,ift6266_ml,aigaion-shorter,specials} |
469 | 904 %\bibliographystyle{plainnat} |
905 \bibliographystyle{unsrtnat} | |
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906 %\bibliographystyle{apalike} |
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907 } |
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908 |
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909 |
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910 \end{document} |