# HG changeset patch
# User fsavard
# Date 1285883696 14400
# Node ID 4672fb6b4385d948ace6a19bd0aadf6c5968ef58
# Parent  9a6abcf143e84391e961bc9740c9e9297c86d647
Changé mlj_submission.tex pour merge

diff -r 9a6abcf143e8 -r 4672fb6b4385 writeup/mlj_submission.tex
--- a/writeup/mlj_submission.tex	Thu Sep 30 17:51:46 2010 -0400
+++ b/writeup/mlj_submission.tex	Thu Sep 30 17:54:56 2010 -0400
@@ -129,7 +129,7 @@
 learning, often in an greedy layer-wise ``unsupervised pre-training''
 stage~\citep{Bengio-2009}.  One of these layer initialization techniques,
 applied here, is the Denoising
-Auto-encoder~(DA)~\citep{VincentPLarochelleH2008} (see Figure~\ref{fig:da}), 
+Auto-encoder~(DA)~\citep{VincentPLarochelleH2008-very-small} (see Figure~\ref{fig:da}), 
 which
 performed similarly or better than previously proposed Restricted Boltzmann
 Machines in terms of unsupervised extraction of a hierarchy of features
@@ -203,7 +203,7 @@
 %\begin{minipage}[b]{0.14\linewidth}
 %\vspace*{-5mm}
 \begin{center}
-\includegraphics[scale=.4]{images/Original.png}\\
+\includegraphics[scale=.4]{Original.png}\\
 {\bf Original}
 \end{center}
 \end{wrapfigure}
@@ -240,7 +240,7 @@
 %\centering
 \begin{center}
 \vspace*{-5mm}
-\includegraphics[scale=.4]{images/Thick_only.png}\\
+\includegraphics[scale=.4]{Thick_only.png}\\
 %{\bf Thickness}
 \end{center}
 \vspace{.6cm}
@@ -268,7 +268,7 @@
 
 \begin{minipage}[b]{0.14\linewidth}
 \centering
-\includegraphics[scale=.4]{images/Slant_only.png}\\
+\includegraphics[scale=.4]{Slant_only.png}\\
 %{\bf Slant}
 \end{minipage}%
 \hspace{0.3cm}
@@ -290,7 +290,7 @@
 %\centering
 %\begin{wrapfigure}[8]{l}{0.15\textwidth}
 \begin{center}
-\includegraphics[scale=.4]{images/Affine_only.png}
+\includegraphics[scale=.4]{Affine_only.png}
 \vspace*{6mm}
 %{\small {\bf Affine \mbox{Transformation}}}
 \end{center}
@@ -320,7 +320,7 @@
 %\centering
 \begin{center}
 \vspace*{5mm}
-\includegraphics[scale=.4]{images/Localelasticdistorsions_only.png}
+\includegraphics[scale=.4]{Localelasticdistorsions_only.png}
 %{\bf Local Elastic Deformation}
 \end{center}
 %\end{wrapfigure}
@@ -347,7 +347,7 @@
 %\begin{wrapfigure}[7]{l}{0.15\textwidth}
 %\vspace*{-5mm}
 \begin{center}
-\includegraphics[scale=.4]{images/Pinch_only.png}\\
+\includegraphics[scale=.4]{Pinch_only.png}\\
 \vspace*{15mm}
 %{\bf Pinch}
 \end{center}
@@ -384,7 +384,7 @@
 \begin{minipage}[t]{0.14\linewidth}
 \centering
 \vspace*{0mm}
-\includegraphics[scale=.4]{images/Motionblur_only.png}
+\includegraphics[scale=.4]{Motionblur_only.png}
 %{\bf Motion Blur}
 \end{minipage}%
 \hspace{0.3cm}\begin{minipage}[t]{0.83\linewidth}
@@ -405,7 +405,7 @@
 \begin{minipage}[t]{0.14\linewidth}
 \centering
 \vspace*{3mm}
-\includegraphics[scale=.4]{images/occlusion_only.png}\\
+\includegraphics[scale=.4]{occlusion_only.png}\\
 %{\bf Occlusion}
 %%\vspace{.5cm}
 \end{minipage}%
@@ -432,7 +432,7 @@
 \begin{center}
 %\centering
 \vspace*{6mm}
-\includegraphics[scale=.4]{images/Bruitgauss_only.png}
+\includegraphics[scale=.4]{Bruitgauss_only.png}
 %{\bf Gaussian Smoothing}
 \end{center}
 %\end{wrapfigure}
@@ -468,7 +468,7 @@
 %\vspace*{-5mm}
 \begin{center}
 \vspace*{1mm}
-\includegraphics[scale=.4]{images/Permutpixel_only.png}
+\includegraphics[scale=.4]{Permutpixel_only.png}
 %{\small\bf Permute Pixels}
 \end{center}
 %\end{wrapfigure}
@@ -495,7 +495,7 @@
 %\hspace*{-3mm}\begin{minipage}[t]{0.18\linewidth}
 %\centering
 \vspace*{0mm}
-\includegraphics[scale=.4]{images/Distorsiongauss_only.png}
+\includegraphics[scale=.4]{Distorsiongauss_only.png}
 %{\small \bf Gauss. Noise}
 \end{center}
 %\end{wrapfigure}
@@ -517,7 +517,7 @@
 \begin{minipage}[t]{0.14\linewidth}
 \centering
 \vspace*{0mm}
-\includegraphics[scale=.4]{images/background_other_only.png}
+\includegraphics[scale=.4]{background_other_only.png}
 %{\small \bf Bg Image}
 \end{minipage}%
 \hspace{0.3cm}\begin{minipage}[t]{0.83\linewidth}
@@ -536,7 +536,7 @@
 \begin{minipage}[t]{0.14\linewidth}
 \centering
 \vspace*{0mm}
-\includegraphics[scale=.4]{images/Poivresel_only.png}
+\includegraphics[scale=.4]{Poivresel_only.png}
 %{\small \bf Salt \& Pepper}
 \end{minipage}%
 \hspace{0.3cm}\begin{minipage}[t]{0.83\linewidth}
@@ -558,7 +558,7 @@
 \begin{center}
 \vspace*{4mm}
 %\hspace*{-1mm}
-\includegraphics[scale=.4]{images/Rature_only.png}\\
+\includegraphics[scale=.4]{Rature_only.png}\\
 %{\bf Scratches}
 \end{center}
 \end{minipage}%
@@ -584,7 +584,7 @@
 \begin{minipage}[t]{0.15\linewidth}
 \centering
 \vspace*{0mm}
-\includegraphics[scale=.4]{images/Contrast_only.png}
+\includegraphics[scale=.4]{Contrast_only.png}
 %{\bf Grey Level \& Contrast}
 \end{minipage}%
 \hspace{3mm}\begin{minipage}[t]{0.85\linewidth}
@@ -791,7 +791,7 @@
 
 \begin{figure}[ht]
 %\vspace*{-2mm}
-\centerline{\resizebox{0.8\textwidth}{!}{\includegraphics{images/denoising_autoencoder_small.pdf}}}
+\centerline{\resizebox{0.8\textwidth}{!}{\includegraphics{denoising_autoencoder_small.pdf}}}
 %\vspace*{-2mm}
 \caption{Illustration of the computations and training criterion for the denoising
 auto-encoder used to pre-train each layer of the deep architecture. Input $x$ of
@@ -840,7 +840,7 @@
 
 \begin{figure}[ht]
 %\vspace*{-2mm}
-\centerline{\resizebox{.99\textwidth}{!}{\includegraphics{images/error_rates_charts.pdf}}}
+\centerline{\resizebox{.99\textwidth}{!}{\includegraphics{error_rates_charts.pdf}}}
 %\vspace*{-3mm}
 \caption{SDAx are the {\bf deep} models. Error bars indicate a 95\% confidence interval. 0 indicates that the model was trained
 on NIST, 1 on NISTP, and 2 on P07. Left: overall results
@@ -855,7 +855,7 @@
 
 \begin{figure}[ht]
 %\vspace*{-3mm}
-\centerline{\resizebox{.99\textwidth}{!}{\includegraphics{images/improvements_charts.pdf}}}
+\centerline{\resizebox{.99\textwidth}{!}{\includegraphics{improvements_charts.pdf}}}
 %\vspace*{-3mm}
 \caption{Relative improvement in error rate due to self-taught learning. 
 Left: Improvement (or loss, when negative)