Add mode collapse to CDCGAN

1 files changed, 33 insertions, 9 deletions

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}