Interpretable Compositional Convolutional Neural Networks

• URL: http://arxiv.org/abs/2107.04474v1
• Date: Fri, 9 Jul 2021 15:01:24 GMT
• Title: Interpretable Compositional Convolutional Neural Networks
• Authors: Wen Shen, Zhihua Wei, Shikun Huang, Binbin Zhang, Jiaqi Fan, Ping Zhao, Quanshi Zhang
• Abstract summary: We propose a method to modify a traditional convolutional neural network (CNN) into an interpretable compositional CNN. In a compositional CNN, each filter is supposed to consistently represent a specific compositional object part or image region with a clear meaning. Our method can be broadly applied to different types of CNNs.
• Score: 20.726080433723922
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The reasonable definition of semantic interpretability presents the core challenge in explainable AI. This paper proposes a method to modify a traditional convolutional neural network (CNN) into an interpretable compositional CNN, in order to learn filters that encode meaningful visual patterns in intermediate convolutional layers. In a compositional CNN, each filter is supposed to consistently represent a specific compositional object part or image region with a clear meaning. The compositional CNN learns from image labels for classification without any annotations of parts or regions for supervision. Our method can be broadly applied to different types of CNNs. Experiments have demonstrated the effectiveness of our method.

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