Interpreting CNN Knowledge via an Explanatory Graph

Authors

  • Quanshi Zhang University of California, Los Angeles
  • Ruiming Cao University of California, Los Angeles
  • Feng Shi University of California, Los Angeles
  • Ying Nian Wu University of California, Los Angeles
  • Song-Chun Zhu University of California, Los Angeles

Keywords:

Convolutional Neural Network, Graphical Model, Interpretable Model

Abstract

This paper learns a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside a pre-trained CNN. Considering that each filter in a conv-layer of a pre-trained CNN usually represents a mixture of object parts, we propose a simple yet efficient method to automatically disentangles different part patterns from each filter, and construct an explanatory graph. In the explanatory graph, each node represents a part pattern, and each edge encodes co-activation relationships and spatial relationships between patterns. More importantly, we learn the explanatory graph for a pre-trained CNN in an unsupervised manner, i.e., without a need of annotating object parts. Experiments show that each graph node consistently represents the same object part through different images. We transfer part patterns in the explanatory graph to the task of part localization, and our method significantly outperforms other approaches.

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Published

2018-04-29

How to Cite

Zhang, Q., Cao, R., Shi, F., Wu, Y. N., & Zhu, S.-C. (2018). Interpreting CNN Knowledge via an Explanatory Graph. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11819