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diff --git a/report/paper.md b/report/paper.md index eaaed12..11d8c36 100644 --- a/report/paper.md +++ b/report/paper.md @@ -113,15 +113,7 @@ We evaluate permutations of the architecture involving: \end{center} \end{figure} - -## Tests on MNIST - -Try **different architectures, hyper-parameters**, and, if necessary, the aspects of **one-sided label -smoothing**, **virtual batch normalization**, balancing G and D. -Please perform qualitative analyses on the generated images, and discuss, with results, what -challenge and how they are specifically addressing. Is there the **mode collapse issue?** - -The effect of dropout for the non-convolutional CGAN architecture does not affect performance as much as in DCGAN, as the images produced, together with the G-D loss remain almost unchanged. Results are presented in figures \ref{fig:cg_drop1_1}, \ref{fig:cg_drop1_2}, \ref{fig:cg_drop2_1}, \ref{fig:cg_drop2_2}. +## Tests on MNIST \begin{figure} \begin{center} @@ -132,23 +124,14 @@ The effect of dropout for the non-convolutional CGAN architecture does not affec \end{center} \end{figure} -\begin{figure} -\begin{center} -\includegraphics[width=24em]{fig/smoothing_ex.pdf} -\includegraphics[width=24em]{fig/smoothing.png} -\caption{One sided label smoothing} -\label{fig:smooth} -\end{center} -\end{figure} - -# Inception Score +### Inception Score Inception score is calculated as introduced by Tim Salimans et. al [@improved]. However as we are evaluating MNIST, we use LeNet as the basis of the inceptioen score. -Inception score is calculated with the logits of the LeNet +We use the logits extracted from LeNet: $$ \textrm{IS}(x) = \exp(\mathbb{E}_x \left( \textrm{KL} ( p(y\mid x) \| p(y) ) \right) ) $$ -## Classifier Architecture Used +### Classifier Architecture Used \begin{table}[] \begin{tabular}{llll} @@ -167,9 +150,34 @@ Medium CGAN+VBN+LS & 0.783 & 4.31 & 10:38 \\ \end{tabular} \end{table} +## Discussion + +### Architecture + +### One Side Label Smoothing + +\begin{figure} +\begin{center} +\includegraphics[width=24em]{fig/smoothing_ex.pdf} +\includegraphics[width=24em]{fig/smoothing.png} +\caption{One sided label smoothing} +\label{fig:smooth} +\end{center} +\end{figure} + + + +### Virtual Batch Normalisation + + +### Dropout +The effect of dropout for the non-convolutional CGAN architecture does not affect performance as much as in DCGAN, nor does it seem to affect the quality of images produced, together with the G-D loss remain almost unchanged. Results are presented in figures \ref{fig:cg_drop1_1}, \ref{fig:cg_drop1_2}, \ref{fig:cg_drop2_1}, \ref{fig:cg_drop2_2}. + **Please measure and discuss the inception scores for the different hyper-parameters/tricks and/or + + # Re-training the handwritten digit classifier ## Results |