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	\myheading{Module 13.5: Guided Backpropagation}
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		\input{modules/Module5/tikz_images/fwd_pass.tex}
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			\begin{itemize}
				\justifying
				\onslide<1->{\item We feed an input to the CNN and do a forward pass}
				\onslide<2->{\item We consider one neuron in some feature map at some layer}
				\onslide<3->{\item We are interested in finding the influence of the input on this neuron}
				\onslide<4->{\item We retain this neuron and set all other neurons in the layer to zero}
			\end{itemize}

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			\input{modules/Module5/tikz_images/back_pass.tex}

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			\begin{itemize}
				\justifying
				\onslide<1->{\item We now backpropogate all the way to the inputs}
				\onslide<2->{\item Recall that during forward pass relu activation allows only positive values to pass \& clamps $-ve$ values to zero}
				\onslide<3->{\item Similarly during backward pass no gradient passes through the dead relu neurons}
				\onslide<4->{\item In guided back propagation any -ve gradients flowing from the upper layer are also set to 0}
			\end{itemize}

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				\input{modules/Module5/tikz_images/guide_bp.tex}
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			\begin{itemize}
				\justifying
				\onslide<1->{\item \textbf{Intuition:} Neglect all the negative influences (gradients) and focus only on the positive influences (gradients)}
				\onslide<2->{\item This gives a better picture of the true influence of the input}
			\end{itemize}

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