\begin{frame}
	\myheading{Module 13.3: Occlusion experiments}
\end{frame}

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\begin{frame}
	\begin{columns}
		\column{0.5\textwidth}
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			% \only<1>{
			% 	\begin{figure}[hbtp]
			% 	\includegraphics[scale=0.15,trim={0cm 0cm 72cm 0cm}, clip=true]{img/s1_4_1.pdf}
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			% 	}
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			\only<1->{
				\input{modules/Module3/tikz_images/occlusion.tex}
			}
			\vspace{-0.5cm}
			\only<1>{
				\begin{figure}
					\includegraphics[scale=0.1,trim={0cm 31.5cm 72cm 0cm}, clip=true]{images/s1_4_1.pdf}
				\end{figure}
			}

			\only<2>{
				\begin{figure}
					\includegraphics[scale=0.1,trim={0cm 31.5cm 55cm 0cm}, clip=true]{images/s1_4_1.pdf}
				\end{figure}
			}

			\only<3>{

				\begin{figure}
					\includegraphics[scale=0.1,trim={0cm 15.5cm 55cm 0cm}, clip=true]{images/s1_4_1.pdf}
				\end{figure}
			}

			\only<4>{

				\begin{figure}
					\includegraphics[scale=0.1,trim={0cm 0cm 55cm 0cm}, clip=true]{images/s1_4_1.pdf}
				\end{figure}
			}
		\end{overlayarea}
		\column{0.5\textwidth}
		\begin{overlayarea}{\textwidth}{\textheight}
			\begin{itemize}
				\justifying
				\item<1-> Typically we are interested in understanding which portions of the image are responsible
				for maximizing the probability of a certain class
				\item<2-> We could occlude (gray out) different patches in the image and see the effect on the predicted probability of the correct class
				\item<3->For example this heat map shows that occluding the face of the dog causes a maximum drop in the prediction probability
				\item<4-> Similar observations are made for other images
			\end{itemize}
		\end{overlayarea}
	\end{columns}

\end{frame}
