Interpretable Convolutional Neural Networks

CVPR 2018 • Quanshi Zhang • Ying Nian Wu • Song-Chun Zhu

This paper proposes a method to modify traditional convolutional neural networks (CNNs) into interpretable CNNs, in order to clarify knowledge representations in high conv-layers of CNNs. In an interpretable CNN, each filter in a high conv-layer represents a certain object part... (read more)

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