Explaining nonlinear classification decisions with deep Taylor decomposition
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文摘
A novel method to explain nonlinear classification decisions in terms of input variables is introduced. The method is based on Taylor expansions and decomposes the output of a deep neural network in terms of input variables. The resulting deep Taylor decomposition can be applied directly to existing neural networks without retraining. The method is tested on two large-scale neural networks for image classification: BVLC CaffeNet and GoogleNet.
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