VisualBackProp: efficient visualization of CNNs
Mariusz Bojarski, Anna Choromanska, Krzysztof Choromanski, Bernard Firner, Larry Jackel, Urs Muller, Karol Zieba
This paper proposes a new method, that we call VisualBackProp, for visualizing which sets of pixels of the input image contribute most to the predictions made by the convolutional neural network (CNN). The method heavily hinges on exploring the intuition that the feature maps contain less and less irrelevant information to the prediction decision when moving deeper in the network. The technique we propose was developed as a debugging tool for CNN-based systems for steering self-driving cars and is therefore required to run in real-time, i.e. the proposed method was designed to require less computations then single forward propagation per image. This makes the presented visualization method a valuable debugging tool which can be easily used during training or inference. We furthermore justify our approach with theoretical argument and theoretically confirm that the proposed method identifies sets of input pixels, rather than individual pixels, that collaboratively con- tribute to the prediction. Our theoretical findings stand in agreement with experimental results. The empirical evaluation shows the plausibility of the proposed approach on road data.