Interpretable Compositional Convolutional Neural Networks
Abstract
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. The code will be released when the paper is accepted.
Cite
Text
Shen et al. "Interpretable Compositional Convolutional Neural Networks." International Joint Conference on Artificial Intelligence, 2021. doi:10.24963/IJCAI.2021/409Markdown
[Shen et al. "Interpretable Compositional Convolutional Neural Networks." International Joint Conference on Artificial Intelligence, 2021.](https://mlanthology.org/ijcai/2021/shen2021ijcai-interpretable/) doi:10.24963/IJCAI.2021/409BibTeX
@inproceedings{shen2021ijcai-interpretable,
title = {{Interpretable Compositional Convolutional Neural Networks}},
author = {Shen, Wen and Wei, Zhihua and Huang, Shikun and Zhang, Binbin and Fan, Jiaqi and Zhao, Ping and Zhang, Quanshi},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2021},
pages = {2971-2978},
doi = {10.24963/IJCAI.2021/409},
url = {https://mlanthology.org/ijcai/2021/shen2021ijcai-interpretable/}
}