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diff --git a/report/paper.md b/report/paper.md index 2ba5401..2177177 100644 --- a/report/paper.md +++ b/report/paper.md @@ -175,19 +175,34 @@ We find a good balance for 12,000 batches. \end{center} \end{figure} -Oscillation on the generator loss is noticeable in figure {fig:cdcloss} due to the discriminator loss approaching zero. One possible +Oscillation on the generator loss is noticeable in figure \ref{fig:cdcloss} due to the discriminator loss approaching zero. One possible adjustment to tackle this issue was balancing G-D training steps, opting for G/D=3, allowing the generator to gain some advantage over the discriminator. This technique allowed to smooth oscillation while producing images of similar quality. A quantitative performance assessment will be performed in the following section. +Using G/D=6 dampens oscillation almost completely leading to the vanishing discriminator's gradient issue. Mode collapse occurs in this specific case as shown on +figure \ref{fig:cdccollapse}. Checking the embeddings extracted from a pretrained LeNet classifier (figure \ref{fig:clustcollapse})we observe low diversity between features of each class, that +tend to collapse to very small regions. \begin{figure} \begin{center} -\includegraphics[width=12em]{fig/cdcloss1.png} -\includegraphics[width=12em]{fig/cdcloss2.png} -\caption{CDCGAN G-D loss; Left G/D=1; Right G/D=3} +\includegraphics[width=8em]{fig/cdcloss1.png} +\includegraphics[width=8em]{fig/cdcloss2.png} +\includegraphics[width=8em]{fig/cdcloss3.png} +\caption{CDCGAN G-D loss; Left G/D=1; Middle G/D=3; Right G/D=6} \label{fig:cdcloss} \end{center} \end{figure} +\begin{figure} +\begin{center} +\includegraphics[width=8em]{fig/cdc_collapse.png} +\includegraphics[width=8em]{fig/cdc_collapse.png} +\includegraphics[width=8em]{fig/cdc_collapse.png} +\caption{CDCGAN G/D=6 mode collapse} +\label{fig:cdccollapse} +\end{center} +\end{figure} + + Virtual Batch Normalization on this architecture was not attempted as it significantly increased the training time (about twice more). Introducing one-sided label smoothing produced very similar results (figure \ref{fig:cdcsmooth}), hence a quantitative performance assessment will need to @@ -209,10 +224,11 @@ calculated training the LeNet classifier under the same conditions across all ex Shallow CGAN & 0.645 & 3.57 & 8:14 \\ Medium CGAN & 0.715 & 3.79 & 10:23 \\ Deep CGAN & 0.739 & 3.85 & 16:27 \\ -\textbf{CDCGAN} & \textbf{0.899} & \textbf{7.41} & 1:05:27 \\ +\textbf{CDCGAN} & \textbf{0.899} & \textbf{7.41} & 1:05:27 \\ Medium CGAN+LS & 0.749 & 3.643 & 10:42 \\ -CDCGAN+LS & 0.846 & 6.63 & 1:12:39 \\ -CCGAN-G/D=3 & 0.849 & 6.59 & 1:04:11 \\ +CDCGAN+LS & 0.846 & 6.63 & 1:12:39 \\ +CCGAN-G/D=3 & 0.849 & 6.59 & 48:11 \\ +CCGAN-G/D=6 & 0.801 & 6.06 & 36:05 \\ Medium CGAN DO=0.1 & 0.761 & 3.836 & 10:36 \\ Medium CGAN DO=0.5 & 0.725 & 3.677 & 10:36 \\ Medium CGAN+VBN & 0.735 & 3.82 & 19:38 \\ @@ -268,7 +284,7 @@ As observed in figure \ref{fig:mix1} we performed two experiments for performanc \end{center} \end{figure} -Both experiments show that training the classification network with the injection of generated data (between 40% and 90%) causes on average a small increase in accuracy of up to 0.2%. In absence of original data the testing accuracy drops significantly to around 40% for both cases. +Both experiments show that training the classification network with the injection of generated data (between 40% and 90%) causes on average a small increase in accuracy of up to 0.2%. In absence of original data the testing accuracy drops significantly to around 40% for both cases. ## Adapted Training Strategy @@ -498,7 +514,15 @@ $$ L_{\textrm{total}} = \alpha L_{\textrm{LeNet}} + \beta L_{\textrm{generator}} \end{center} \end{figure} -\begin{figure} +\begin{figure}[H] +\begin{center} +\includegraphics[width=18em]{fig/clustcollapse.png} +\caption{CDCGAN G/D=6 Embeddings through LeNet} +\label{fig:clustcollapse} +\end{center} +\end{figure} + +\begin{figure}[H] \begin{center} \includegraphics[width=8em]{fig/cdcsmooth.png} \caption{CDCGAN+LS outputs 12000 batches} |