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kumar-shridhar committed Dec 4, 2018
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Expand Up @@ -4,6 +4,8 @@ \chapter{Experiment Specifications}

\section*{Bayesian Settings}

\subsection{Image Recognition}

\begin{table}[h!]
\centering
\renewcommand{\arraystretch}{2}
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Sample size can vary from 10 to 25 as this range provided the best results. However, it can be played around with. For most of our experiments it is either 10 or 25 unless specified otherwise.

\subsection{Image Super Resolution}

\begin{table}[h!]
\centering
\renewcommand{\arraystretch}{2}
\begin{tabular}[c]{c | c}
\hline
variable & value \\ [0.5ex]
\hline
learning rate & 0.01\\

epochs & 200 \\

batch size & 64 \\

upscale factor & 3 \\

loss & Mean Squared Error \\

seed & 123 \\

$(\alpha \mu^2)_{init}$ of approximate posterior $q_{\theta}(w|\mathcal{D})$ & -10 \\

optimizer & Adam \cite{kingma2014adam} \\

$\lambda$ in $\ell$-2 normalisation & 0.0005 \\

$\beta_i$ & $\frac{2^{M-i}}{2^M-1}$ \cite{blundell2015weight} \\ [1ex]
\hline
\end{tabular}
\renewcommand{\arraystretch}{2}
\end{table}


\subsection{Generative Adversarial Network}

\begin{table}[h!]
\centering
\renewcommand{\arraystretch}{2}
\begin{tabular}[c]{c | c}
\hline
variable & value \\ [0.5ex]
\hline
learning rate & 0.001\\

epochs & 100 \\

batch size & 64 \\

image size & 64 \\

latent vector (nz) & 100 \\

number of generator factor (ndf) & 64 \\

number of discriminator factor (ndf) & 64 \\

upscale factor & 3 \\

loss & Mean Squared Error \\

number of channels (nc) & 3 \\

$(\alpha \mu^2)_{init}$ of approximate posterior $q_{\theta}(w|\mathcal{D})$ & -10 \\

optimizer & Adam \cite{kingma2014adam} \\

$\lambda$ in $\ell$-2 normalisation & 0.0005 \\

$\beta_i$ & $\frac{2^{M-i}}{2^M-1}$ \cite{blundell2015weight} \\ [1ex]
\hline
\end{tabular}
\renewcommand{\arraystretch}{2}
\end{table}

\section*{Non Bayesian Settings}

\subsection{Image Recognition}

\begin{table}[h!]
\centering
\renewcommand{\arraystretch}{2}
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