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\title{Generating and Exploiting Perturbed Training Data for Deep Architectures}
\author{The IFT6266 Gang}
\date{April 2010, Technical Report, Dept. IRO, U. Montreal}

\maketitle

\begin{abstract}

\end{abstract}

\section{Introduction}

\section{Perturbation and Transformation of Character Images}

\subsection{Affine Transformations}
\subsection{Adding Slant}
\subsection{Local Elastic Deformations}
\subsection{Changing Thickness}
\subsection{Occlusion}
\subsection{Background Images}
\subsection{Salt and Pepper Noise}
\subsection{Spatially Gaussian Noise}
\subsection{Color and Contrast Changes}


\section{Learning Algorithms for Deep Architectures}

\section{Experimental Setup}

\subsection{Training Datasets}

\subsubsection{Data Sources}

\begin{itemize}
\item {\bf NIST}
\item {\bf Fonts}
\item {\bf Captchas}
\item {\bf OCR data}
\end{itemize}

\subsubsection{Data Sets}
\begin{itemize}
\item {\bf NIST}
\item {\bf P07}
\item {\bf NISTP} {\em ne pas utiliser PNIST mais NISTP, pour rester politically correct...}
\end{itemize}

\subsection{Models and their Hyperparameters}

\subsubsection{Multi-Layer Perceptrons (MLP)}

\subsubsection{Stacked Denoising Auto-Encoders (SDAE)}

\section{Experimental Results}

\subsection{SDAE vs MLP}

\subsection{Perturbed Training Data More Helpful for SDAE}

\subsection{Training with More Classes than Necessary}

\section{Conclusions}

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