ift6266: writeup/nips2010_submission.tex comparison

comparison writeup/nips2010_submission.tex @ 499:2b58eda9fc08

changements de Myriam

author	Yoshua Bengio <bengioy@iro.umontreal.ca>
date	Tue, 01 Jun 2010 12:12:52 -0400
parents	5764a2ae1fb5
children	8479bf822d0e

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 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 character images, including not only affine transformations but
 also slant, local elastic deformations, changes in thickness, background
-images, color, contrast, occlusion, and various types of pixel and
+images, grey level changes, contrast, occlusion, and various types of pixel and
 spatially correlated noise. The out-of-distribution examples are
 obtained by training with these highly distorted images or
 by including object classes different from those in the target test set.
 \end{abstract}
 \vspace*{-2mm}
 This filter is only applied only 15\% of the time. When it is applied, 50\%
 of the time, only one patch image is generated and applied. In 30\% of
 cases, two patches are generated, and otherwise three patches are
 generated. The patch is applied by taking the maximal value on any given
 patch or the original image, for each of the 32x32 pixel locations.\\
-{\bf Color and Contrast Changes.}
+{\bf Grey Level and Contrast Changes.}
 This filter changes the contrast and may invert the image polarity (white
 on black to black on white). The contrast $C$ is defined here as the
 difference between the maximum and the minimum pixel value of the image.
 Contrast $\sim U[1-0.85 \times complexity,1]$ (so contrast $\geq 0.15$).
 The image is normalized into $[\frac{1-C}{2},1-\frac{1-C}{2}]$. The
 polarity is inverted with $0.5$ probability.
+\iffalse
 \begin{figure}[h]
 \resizebox{.99\textwidth}{!}{\includegraphics{images/example_t.png}}\\
 \caption{Illustration of the pipeline of stochastic
 transformations applied to the image of a lower-case t
 (the upper left image). Each image in the pipeline (going from
 left to right, first top line, then bottom line) shows the result
 of applying one of the modules in the pipeline. The last image
 (bottom right) is used as training example.}
 \label{fig:pipeline}
 \end{figure}
+\fi
 \begin{figure}[h]
 \resizebox{.99\textwidth}{!}{\includegraphics{images/transfo.png}}\\
 \caption{Illustration of each transformation applied alone to the same image
 of an upper-case h (top left). First row (from left to right) : original image, slant,
-thickness, affine transformation, local elastic deformation; second row (from left to right) :
+thickness, affine transformation (translation, rotation, shear),
+local elastic deformation; second row (from left to right) :
 pinch, motion blur, occlusion, pixel permutation, Gaussian noise; third row (from left to right) :
 background image, salt and pepper noise, spatially Gaussian noise, scratches,
-color and contrast changes.}
+grey level and contrast changes.}
 \label{fig:transfo}
 \end{figure}
 \vspace*{-1mm}
 the MNIST digits classification task~\citep{Hinton06,ranzato-07,Bengio-nips-2006,Salakhutdinov+Hinton-2009},
 with 60~000 examples, and variants involving 10~000
 examples~\cite{Larochelle-jmlr-toappear-2008,VincentPLarochelleH2008}, we want
 to focus here on the case of much larger training sets, from 10 times to
 to 1000 times larger.  The larger datasets are obtained by first sampling from
-a {\em data source} (NIST characters, scanned machine printed characters, characters
+a {\em data source}: {\bf NIST} (NIST database 19), {\bf Fonts}, {\bf Captchas},
-from fonts, or characters from captchas) and then optionally applying some of the
+and {\bf OCR data} (scanned machine printed characters). Once a character
-above transformations and/or noise processes.
+is sampled from one of these sources (chosen randomly), a pipeline of
+the above transformations and/or noise processes is applied to the
+image.
 \vspace*{-1mm}
 \subsection{Data Sources}
 \vspace*{-1mm}

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