From 3fef722ed752d2369d62c893cc0c4d610a04921a Mon Sep 17 00:00:00 2001
From: nunzip <np.scarh@gmail.com>
Date: Thu, 7 Mar 2019 23:59:26 +0000
Subject: Fix pictures

---
 report/paper.md | 95 +++++++++++++++++++++++++++++++++++++++++++++++++++------
 1 file changed, 86 insertions(+), 9 deletions(-)

diff --git a/report/paper.md b/report/paper.md
index 7a26e55..f079f95 100644
--- a/report/paper.md
+++ b/report/paper.md
@@ -3,6 +3,7 @@
 In this coursework we present two variants of the GAN architecture - DCGAN and CGAN, applied to the MNIST dataaset and evaluate performance metrics across various optimisations techniques. The MNIST dataset contains 60,000 training images and 10,000 testing images of size 28x28, spread across ten classes representing the ten handwritten digits.
 
 ## GAN
+
 Generative Adversarial Networks present a system of models which learn to output data, similar to training data. A trained GAN takes noise as an input and is able to provide an output with the same dimensions and ideally features as the samples it has been trained with.
 
 GAN's employ two neural networks - a *discriminator* and a *generator* which contest in a zero-sum game. The task of the *discriminator* is to distinguish generated images from real images, while the task of the generator is to produce realistic images which are able to fool the discriminator.
@@ -85,6 +86,23 @@ the simple GAN presented in the introduction.
 
 ## CGAN Architecture description
 
+\begin{figure}
+\begin{center}
+\includegraphics[width=24em]{fig/CGAN_arch.pdf}
+\caption{CGAN Architecture}
+\label{fig:cganarc}
+\end{center}
+\end{figure}
+
+\begin{figure}
+\begin{center}
+\includegraphics[width=24em]{fig/CDCGAN_arch.pdf}
+\caption{Deep Convolutional CGAN Architecture}
+\label{fig:cdcganarc}
+\end{center}
+\end{figure}
+
+
 ## Tests on MNIST
 
 Try **different architectures, hyper-parameters**, and, if necessary, the aspects of **one-sided label
@@ -94,8 +112,25 @@ challenge and how they are specifically addressing. Is there the **mode collapse
 
 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}.
 
-# Inception Score
+\begin{figure}
+\begin{center}
+\includegraphics[width=24em]{fig/med_cgan_ex.pdf}
+\includegraphics[width=24em]{fig/med_cgan.png}
+\caption{Medium CGAN}
+\label{fig:cmed}
+\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
 
 ## Classifier Architecture Used
 
@@ -134,6 +169,30 @@ cDCGAN+VB+LS 7.3			.
 Retrain with different portions and test BOTH fake and real queries. Please **vary** the portions
 of the real training and synthetic images, e.g. 10%, 20%, 50%, and 100%, of each.
 
+\begin{figure}
+\begin{center}
+\includegraphics[width=24em]{fig/mix.png}
+\caption{Mix training}
+\label{fig:mix1}
+\end{center}
+\end{figure}
+
+\begin{figure}
+\begin{center}
+\includegraphics[width=24em]{fig/mix_zoom.png}
+\caption{Mix training zoom}
+\label{fig:mix2}
+\end{center}
+\end{figure}
+
+\begin{figure}
+\begin{center}
+\includegraphics[width=24em]{fig/mix_scores.png}
+\caption{Mix training scores}
+\label{fig:mix3}
+\end{center}
+\end{figure}
+
 ## Adapted Training Strategy
 
 *Using even a small number of real samples per class would already give a high recognition rate,
@@ -205,6 +264,24 @@ architecture and loss function?
 \end{center}
 \end{figure}
 
+\begin{figure}
+\begin{center}
+\includegraphics[width=24em]{fig/short_dcgan_ex.pdf}
+\includegraphics[width=24em]{fig/short_dcgan.png}
+\caption{Shallow DCGAN}
+\label{fig:dcshort}
+\end{center}
+\end{figure}
+
+\begin{figure}
+\begin{center}
+\includegraphics[width=24em]{fig/long_dcgan_ex.pdf}
+\includegraphics[width=24em]{fig/long_dcgan.png}
+\caption{Deep DCGAN}
+\label{fig:dclong}
+\end{center}
+\end{figure}
+
 \begin{figure}
 \begin{center}
 \includegraphics[width=24em]{fig/dcgan_dropout01_gd.png}
@@ -239,19 +316,19 @@ architecture and loss function?
 
 \begin{figure}
 \begin{center}
-\includegraphics[width=24em]{fig/short_dcgan_ex.pdf}
-\includegraphics[width=24em]{fig/short_dcgan.png}
-\caption{Shallow DCGAN}
-\label{fig:dcshort}
+\includegraphics[width=24em]{fig/short_cgan_ex.pdf}
+\includegraphics[width=24em]{fig/short_cgan.png}
+\caption{Shallow CGAN}
+\label{fig:cshort}
 \end{center}
 \end{figure}
 
 \begin{figure}
 \begin{center}
-\includegraphics[width=24em]{fig/long_dcgan_ex.pdf}
-\includegraphics[width=24em]{fig/long_dcgan.png}
-\caption{Deep DCGAN}
-\label{fig:dclong}
+\includegraphics[width=24em]{fig/long_cgan_ex.pdf}
+\includegraphics[width=24em]{fig/long_cgan.png}
+\caption{Deep CGAN}
+\label{fig:clong}
 \end{center}
 \end{figure}
 
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