Interpreting CNNs via Decision Trees

CVPR 2019 Quanshi ZhangYu YangHaotian MaYing Nian Wu

This paper aims to quantitatively explain rationales of each prediction that is made by a pre-trained convolutional neural network (CNN). We propose to learn a decision tree, which clarifies the specific reason for each prediction made by the CNN at the semantic level... (read more)

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