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Basic text for section 3, add a bit more detail to section 4.2.2
author Arnaud Bergeron <abergeron@gmail.com>
date Fri, 30 Apr 2010 16:24:30 -0400
parents 0282882aa91f
children e4eb3ee7a0cf
rev   line source
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1 \documentclass[12pt,letterpaper]{article}
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2 \usepackage[utf8]{inputenc}
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3 \usepackage{graphicx}
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4 \usepackage{times}
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5 \usepackage{mlapa}
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6
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7 \begin{document}
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8 \title{Generating and Exploiting Perturbed Training Data for Deep Architectures}
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9 \author{The IFT6266 Gang}
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10 \date{April 2010, Technical Report, Dept. IRO, U. Montreal}
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11
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12 \maketitle
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13
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14 \begin{abstract}
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15 Recent theoretical and empirical work in statistical machine learning has
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16 demonstrated the importance of learning algorithms for deep
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17 architectures, i.e., function classes obtained by composing multiple
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18 non-linear transformations. In the area of handwriting recognition,
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19 deep learning algorithms
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20 had been evaluated on rather small datasets with a few tens of thousands
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21 of examples. Here we propose a powerful generator of variations
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22 of examples for character images based on a pipeline of stochastic
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23 transformations that include not only the usual affine transformations
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24 but also the addition of slant, local elastic deformations, changes
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25 in thickness, background images, color, contrast, occlusion, and
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26 various types of pixel and spatially correlated noise.
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27 We evaluate a deep learning algorithm (Stacked Denoising Autoencoders)
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28 on the task of learning to classify digits and letters transformed
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29 with this pipeline, using the hundreds of millions of generated examples
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30 and testing on the full NIST test set.
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31 We find that the SDA outperforms its
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32 shallow counterpart, an ordinary Multi-Layer Perceptron,
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33 and that it is better able to take advantage of the additional
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34 generated data.
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35 \end{abstract}
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36
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37 \section{Introduction}
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38
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39 Deep Learning has emerged as a promising new area of research in
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40 statistical machine learning (see~\emcite{Bengio-2009} for a review).
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41 Learning algorithms for deep architectures are centered on the learning
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42 of useful representations of data, which are better suited to the task at hand.
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43 This is in great part inspired by observations of the mammalian visual cortex,
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44 which consists of a chain of processing elements, each of which is associated with a
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45 different representation. In fact,
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46 it was found recently that the features learnt in deep architectures resemble
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47 those observed in the first two of these stages (in areas V1 and V2
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48 of visual cortex)~\cite{HonglakL2008}.
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49 Processing images typically involves transforming the raw pixel data into
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50 new {\bf representations} that can be used for analysis or classification.
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51 For example, a principal component analysis representation linearly projects
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52 the input image into a lower-dimensional feature space.
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53 Why learn a representation? Current practice in the computer vision
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54 literature converts the raw pixels into a hand-crafted representation
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55 (e.g.\ SIFT features~\cite{Lowe04}), but deep learning algorithms
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56 tend to discover similar features in their first few
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57 levels~\cite{HonglakL2008,ranzato-08,Koray-08,VincentPLarochelleH2008-very-small}.
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58 Learning increases the
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59 ease and practicality of developing representations that are at once
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60 tailored to specific tasks, yet are able to borrow statistical strength
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61 from other related tasks (e.g., modeling different kinds of objects). Finally, learning the
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62 feature representation can lead to higher-level (more abstract, more
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63 general) features that are more robust to unanticipated sources of
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64 variance extant in real data.
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65
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66 Whereas a deep architecture can in principle be more powerful than a shallow
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67 one in terms of representation, depth appears to render the training problem
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68 more difficult in terms of optimization and local minima.
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69 It is also only recently that
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70 successful algorithms were proposed to overcome some of these
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71 difficulties.
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72
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73 \section{Perturbation and Transformation of Character Images}
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74
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75 \subsection{Adding Slant}
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76 In order to mimic a slant effect, we simply shift each row of the image proportionnaly to its height.
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77 The coefficient is randomly sampled according to the complexity level and can be negatif or positif with equal probability.
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78
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79 \subsection{Changing Thickness}
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80 To change the thickness of the characters we used morpholigical operators: dilation and erosion~\cite{Haralick87,Serra82}.
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81 The basic idea of such transform is, for each pixel, to multiply in the element-wise manner its neighbourhood with a matrix called the structuring element.
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82 Then for dilation we remplace the pixel value by the maximum of the result, or the minimum for erosion.
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83 This will dilate or erode objects in the image, the strength of the transform only depends on the structuring element.
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84 We used ten different structural elements with various shapes (the biggest is $5\times5$).
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85 for each image, we radomly sample the operator type (dilation or erosion) and one structural element
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86 from a subset depending of the complexity (the higher the complexity, the biggest the structural element can be).
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87 Erosion allows only the five smallest structural elements because when the character is too thin it may erase it completly.
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88
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89 \subsection{Affine Transformations}
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90 We generate an affine transform matrix according to the complexity level, then we apply it directly to the image.
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91 This allows to produce scaling, translation, rotation and shearing variances. We took care that the maximum rotation applied
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92 to the image is low enough not to confuse classes.
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94 \subsection{Local Elastic Deformations}
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95
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96 This filter induces a "wiggly" effect in the image. The description here will be brief, as the algorithm follows precisely what is described in \cite{SimardSP03}.
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98 The general idea is to generate two "displacements" fields, for horizontal and vertical displacements of pixels. Each of these fields has the same size as the original image.
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100 When generating the transformed image, we'll loop over the x and y positions in the fields and select, as a value, the value of the pixel in the original image at the (relative) position given by the displacement fields for this x and y. If the position we'd retrieve is outside the borders of the image, we use a 0 value instead.
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102 To generate a pixel in either field, first a value between -1 and 1 is chosen from a uniform distribution. Then all the pixels, in both fields, is multiplied by a constant $\alpha$ which controls the intensity of the displacements (bigger $\alpha$ translates into larger wiggles).
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104 As a final step, each field is convoluted with a Gaussian 2D kernel of standard deviation $\sigma$. Visually, this results in a "blur" filter. This has the effect of making values next to each other in the displacement fields similar. In effect, this makes the wiggles more coherent, less noisy.
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106 As displacement fields were long to compute, 50 pairs of fields were generated per complexity in increments of 0.1 (50 pairs for 0.1, 50 pairs for 0.2, etc.), and afterwards, given a complexity, we selected randomly among the 50 corresponding pairs.
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108 $\sigma$ and $\alpha$ were linked to complexity through the formulas $\alpha = \sqrt[3]{complexity} \times 10.0$ and $\sigma = 10 - 7 \times \sqrt[3]{complexity}$.
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110 \subsection{Motion Blur}
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112 This is a GIMP filter we applied, a "linear motion blur" in GIMP terminology. The description will be brief as it is a well-known filter.
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114 This algorithm has two input parameters, $length$ and $angle$. The value of a pixel in the final image is the mean value of the $length$ first pixels found by moving in the $angle$ direction. An approximation of this idea is used, as we won't fall onto precise pixels by following that direction. This is done using the Bresenham line algorithm.
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116 The angle, in our case, is chosen from a uniform distribution over $[0,360]$ degrees. The length, though, depends on the complexity; it's sampled from a Gaussian distribution of mean 0 and standard deviation $\sigma = 3 \times complexity$.
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118 \subsection{Pinch}
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120 This is another GIMP filter we used. The filter is in fact named "Whirl and pinch", but we don't use the "whirl" part (whirl is set to 0). As described in GIMP, a pinch is "similar to projecting the image onto an elastic surface and pressing or pulling on the center of the surface".
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122 Mathematically, for a square input image, think of drawing a circle of radius $r$ around a center point $C$. Any point (pixel) $P$ belonging to that disk (region inside circle) will have its value recalculated by taking the value of another "source" pixel in the original image. The position of that source pixel is found on the line thats goes through $C$ and $P$, but at some other distance $d_2$. Define $d_1$ to be the distance between $P$ and $C$. $d_2$ is given by $d_2 = sin(\frac{\pi{}d_1}{2r})^{-pinch} \times d_1$, where $pinch$ is a parameter to the filter.
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123
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124 If the region considered is not square then, before computing $d_2$, the smallest dimension (x or y) is stretched such that we may consider the region as if it was square. Then, after $d_2$ has been computed and corresponding components $d_2\_x$ and $d_2\_y$ have been found, the component corresponding to the stretched dimension is compressed back by an inverse ratio.
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125
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126 The actual value is given by bilinear interpolation considering the pixels around the (non-integer) source position thus found.
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127
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128 The value for $pinch$ in our case was given by sampling from an uniform distribution over the range $[-complexity, 0.7 \times complexity]$.
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129
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130
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131 \subsection{Occlusion}
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132
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133 This filter selects random parts of other (hereafter "occlusive") letter images and places them over the original letter (hereafter "occluded") image. To be more precise, having selected a subregion of the occlusive image and a desination position in the occluded image, to determine the final value for a given overlapping pixel, it selects whichever pixel is the lightest. As a reminder, the background value is 0, black, so the value nearest to 1 is selected.
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134
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135 To select a subpart of the occlusive image, four numbers are generated. For compability with the code, we'll call them "haut", "bas", "gauche" and "droite" (respectively meaning top, bottom, left and right). Each of these numbers is selected according to a Gaussian distribution of mean $8 \times complexity$ and standard deviation $2$. This means the largest the complexity is, the biggest the occlusion will be. The absolute value is taken, as the numbers must be positive, and the maximum value is capped at 15.
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136
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137 These four sizes collectively define a window centered on the middle pixel of the occlusive image. This is the part that will be extracted as the occlusion.
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139 The next step is to select a destination position in the occluded image. Vertical and horizontal displacements $y\_arrivee$ and $x\_arrivee$ are selected according to Gaussian distributions of mean 0 and of standard deviations of, respectively, 3 and 2. Then an horizontal placement mode, $endroit$ (meaning location), is selected to be of three values meaning left, middle or right.
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140
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141 If $endroit$ is "middle", the occlusion will be horizontally centered around the horizontal middle of the occluded image, then shifted according to $x\_arrivee$. If $endroit$ is "left", it will be placed on the left of the occluded image, then displaced right according to $x\_arrivee$. The contrary happens if $endroit$ is $right$.
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143 In both the horizontal and vertical positionning, the maximum position in either direction is such that the selected occlusion won't go beyond the borders of the occluded image.
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144
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145 This filter has a probability of not being applied, at all, of 60\%.
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146
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147 \subsection{Background Images}
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149 This transformation adds a random background behind the letter. The background is chosen by first selecting, at random, an image from a set of images. Then we choose a 32x32 subregion of that image as the background image (by sampling x and y positions uniformly while making sure not to cross image borders).
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151 To combine the original letter image and the background image, contrast adjustments are made. We first get the maximal values (i.e. maximal intensity) for both the original image and the background image, $maximage$ and $maxbg$. We also have a parameter, $contrast$, given by sampling from a uniform distribution over $[complexity, 1]$.
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153 Once we have all these numbers, we first adjust the values for the background image. Each pixel value is multiplied by $\frac{max(maximage - contrast, 0)}{maxbg}$. Therefore the higher the contrast, the darkest the background will be.
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155 The final image is found by taking the brightest (i.e. value nearest to 1) pixel from either the background image or the corresponding pixel in the original image.
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156
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157 \subsection{Salt and Pepper Noise}
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159 This filter adds noise to the image by randomly selecting a certain number of them and, for those selected pixels, assign a random value according to a uniform distribution over the $[0,1]$ ranges. This last distribution does not change according to complexity. Instead, the number of selected pixels does: the proportion of changed pixels corresponds to $complexity / 5$, which means, as a maximum, 20\% of the pixels will be randomized. On the lowest extreme, no pixel is changed.
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161 This filter also has a probability of not being applied, at all, of 25\%.
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162
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163 \subsection{Spatially Gaussian Noise}
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164 \subsection{Color and Contrast Changes}
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165
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166 \begin{figure}[h]
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167 \resizebox{.99\textwidth}{!}{\includegraphics{images/example_t.png}}\\
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168 \caption{Illustration of the pipeline of stochastic
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169 transformations applied to the image of a lower-case t
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170 (the upper left image). Each image in the pipeline (going from
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171 left to right, first top line, then bottom line) shows the result
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172 of applying one of the modules in the pipeline. The last image
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173 (bottom right) is used as training example.}
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174 \label{fig:pipeline}
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175 \end{figure}
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176
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177 \section{Learning Algorithms for Deep Architectures}
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178
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179 Learning for deep network has long been a problem since well-known learning algorithms do not generalize well on deep architectures.
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180 Using these training algorithms on deep network usually yields to a worse generalization than on shallow networks.
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181 Recently, new initialization techniques have been discovered that enable better generalization overall.
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182
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183 One of these initialization techniques is denoising auto-encoders.
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184 The principle is that each layer starting from the bottom is trained to encode and decode their input and the encoding part is kept as initialization for the weights and bias of the network.
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185 For more details see section \ref{SdA}.
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186
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187 After initialization is done, standard training algorithms work.
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188 In this case, since we have large data sets we use stochastic gradient descent.
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189 This resemble minibatch training except that the batches are selected at random.
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190 To speed up computation, we randomly pre-arranged examples in batches and used those for all training experiments.
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191
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192 \section{Experimental Setup}
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193
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194 \subsection{Training Datasets}
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195
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196 \subsubsection{Data Sources}
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197
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198 \begin{itemize}
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199 \item {\bf NIST}
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200 \item {\bf Fonts}
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201 \item {\bf Captchas}
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202 \item {\bf OCR data}
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203 \end{itemize}
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204
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205 \subsubsection{Data Sets}
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206 \begin{itemize}
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207 \item {\bf NIST}
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208 \item {\bf P07}
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209 \item {\bf NISTP} {\em ne pas utiliser PNIST mais NISTP, pour rester politically correct...}
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210 \end{itemize}
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211
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212 \subsection{Models and their Hyperparameters}
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213
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214 \subsubsection{Multi-Layer Perceptrons (MLP)}
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215
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216 An MLP is a family of functions that are described by stacking layers of of a function similar to
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217 $$g(x) = \tanh(b+Wx)$$
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218 The input, $x$, is a $d$-dimension vector.
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219 The output, $g(x)$, is a $m$-dimension vector.
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220 The parameter $W$ is a $m\times d$ matrix and is called the weight matrix.
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221 The parameter $b$ is a $m$-vector and is called the bias vector.
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222 The non-linearity (here $\tanh$) is applied element-wise to the output vector.
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223 Usually the input is referred to a input layer and similarly for the output.
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224 You can of course chain several such functions to obtain a more complex one.
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225 Here is a common example
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226 $$f(x) = c + V\tanh(b+Wx)$$
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227 In this case the intermediate layer corresponding to $\tanh(b+Wx)$ is called a hidden layer.
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228 Here the output layer does not have the same non-linearity as the hidden layer.
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229 This is a common case where some specialized non-linearity is applied to the output layer only depending on the task at hand.
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230
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231 If you put 3 or more hidden layers in such a network you obtain what is called a deep MLP.
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232 The parameters to adapt are the weight matrix and the bias vector for each layer.
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233
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234 \subsubsection{Stacked Denoising Auto-Encoders (SDAE)}
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235 \label{SdA}
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236
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237 Auto-encoders are essentially a way to initialize the weights of the network to enable better generalization.
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238 This is essentially unsupervised training where the layer is made to reconstruct its input through and encoding and decoding phase.
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239 Denoising auto-encoders are a variant where the input is corrupted with random noise but the target is the uncorrupted input.
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240 The principle behind these initialization methods is that the network will learn the inherent relation between portions of the data and be able to represent them thus helping with whatever task we want to perform.
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241
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242 An auto-encoder unit is formed of two MLP layers with the bottom one called the encoding layer and the top one the decoding layer.
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243 Usually the top and bottom weight matrices are the transpose of each other and are fixed this way.
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244 The network is trained as such and, when sufficiently trained, the MLP layer is initialized with the parameters of the encoding layer.
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245 The other parameters are discarded.
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246
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247 The stacked version is an adaptation to deep MLPs where you initialize each layer with a denoising auto-encoder starting from the bottom.
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248 During the initialization, which is usually called pre-training, the bottom layer is treated as if it were an isolated auto-encoder.
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249 The second and following layers receive the same treatment except that they take as input the encoded version of the data that has gone through the layers before it.
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250 For additional details see \cite{vincent:icml08}.
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251
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252 \section{Experimental Results}
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253
392
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254 \subsection{SDA vs MLP}
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255
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256 \begin{center}
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257 \begin{tabular}{lcc}
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258 & train w/ & train w/ \\
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259 & NIST & P07 + NIST \\ \hline
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260 SDA & & \\ \hline
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261 MLP & & \\ \hline
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262 \end{tabular}
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263 \end{center}
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264
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265 \subsection{Perturbed Training Data More Helpful for SDAE}
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266
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267 \subsection{Training with More Classes than Necessary}
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268
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269 \section{Conclusions}
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270
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271 \bibliography{strings,ml,aigaion,specials}
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272 \bibliographystyle{mlapa}
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273
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274 \end{document}