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We present an application of the Layer-wise Relevance Propagation (LRP)algorithm to state of the art deep convolutional neural networks and FisherVector classifiers to compare the image perception and prediction strategies ofboth classifiers with the use of visualized heatmaps. Layer-wise RelevancePropagation (LRP) is a method to compute scores for individual components of aninput image, denoting their contribution to the prediction of the classifierfor one particular test point. We demonstrate the impact of different choicesof decomposition cut-off points during the LRP-process, controlling theresolution and semantics of the heatmap on test images from the PASCAL VOC 2007test data set.

Controlling Explanatory Heatmap Resolution and Semantics via Decomposition Depth