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Several approaches for understanding and visualizing Convolutional Networks have been developed in the literature, partly as a response the common criticism that the learned features in a Neural Network are not interpretable. In this section we briefly survey some of these approaches and related work. The most straight-forward visualization technique is to show the activations of the network during the forward pass.

Fooling ConvNets

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Visualizing what ConvNets learn

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Introduction

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