\begin{frame}
	\myheading{Module 8.5 : Dataset augmentation}
\end{frame}

\begin{frame}
\vspace{4em}
	\begin{overlayarea}{\textwidth}{\textheight}
		\begin{block}{Different forms of regularization}
			\begin{itemize}
				\item $l_2$ regularization
				\item \textcolor<2->{red}{Dataset augmentation}
				\item Parameter Sharing and tying
				\item Adding Noise to the inputs 
				\item Adding Noise to the outputs 
				\item Early stopping
				\item Ensemble methods
				\item Dropout
			\end{itemize}
		\end{block}
	\end{overlayarea}
\end{frame}
\begin{frame}
	\begin{columns}
		\column{0.3\textwidth}
		\begin{center}
			\input{modules/Module5/tikz_images/data}
		\end{center}
		\onslide<2->{\hspace{1em}[given training data]\\}
		\onslide<12->{We exploit the fact that certain transformations to the image do not change the label of the image.}
					
		\column{0.7\textwidth}
		\onslide<3->{
			\hspace{2em}
			\vspace{2em}
			\begin{overlayarea}{\textwidth}{\textheight}
				\input{modules/Module5/tikz_images/shifted}
				\onslide<11->{[augmented data = created using some knowledge of the task]}
			\end{overlayarea}}
	\end{columns}    
\end{frame}
		
\begin{frame}
	\vspace{4em}    
	\begin{overlayarea}{\textwidth}{\textheight}
						
		\begin{itemize}
			\item <1-> Typically, More data = better learning
			\item <2->  Works well for image classification / object recognition tasks
			\item <3->  Also shown to work well for speech
			\item <4->  For some tasks it may not be clear how to generate such data
		\end{itemize}
						
	\end{overlayarea}
\end{frame}
