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Delving Deep into Interpreting Neural Nets with Piece-Wise Affine Representation

Deep convolutional neural networks (CNNs) are now ubiquitous in computer vision problems. However, these models usually describe very complicated functions of the input images. For a number of application, it is of utmost importance to be able to explain the decisions of a network, e.g. by highlighting the most relevant pixels in an image or a feature map w.r.t. a particular class. In this paper, we show that CNNs locally describe piece-wise affine functions of each pixel, whose coefficient and bias can be retrieved analytically. We apply our methodology on several popular CNNs and draw interesting conclusions on the relative contributions of pixels and biases for these networks.