Towards Axiomatic, Hierarchical, and Symbolic Explanation for Deep Models

Jie Ren, Mingjie Li, Qirui Chen, Huiqi Deng, Quanshi Zhang

Abstract: This paper proposes a hierarchical and symbolic And-Or graph (AOG) to objectively explain the internal logic encoded by a well-trained deep model for inference. We first define the objectiveness of an explainer model in game theory, and we develop a rigorous representation of the And-Or logic encoded by the deep model. The objectiveness and trustworthiness of the AOG explainer are both theoretically guaranteed and experimentally verified. Furthermore, we propose several techniques to boost the conciseness of the explanation.