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Layer-wise relevance propagation is a framework which allows to decompose theprediction of a deep neural network computed over a sample, e.g. an image, downto relevance scores for the single input dimensions of the sample such assubpixels of an image. While this approach can be applied directly togeneralized linear mappings, product type non-linearities are not covered. Thispaper proposes an approach to extend layer-wise relevance propagation to neuralnetworks with local renormalization layers, which is a very common product-typenon-linearity in convolutional neural networks. We evaluate the proposed methodfor local renormalization layers on the CIFAR-10, Imagenet and MIT Placesdatasets.

Layer-wise Relevance Propagation for Neural Networks with Local Renormalization Layers