Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (8): 1-9.DOI: 10.3778/j.issn.1002-8331.2012-0357

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Survey of Interpretability Research on Deep Learning Models

ZENG Chunyan, YAN Kang, WANG Zhifeng, YU Yan, JI Chunmei   

  1. 1.Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan 430068, China
    2.Department of Digital Media Technology, Central China Normal University, Wuhan 430079, China
    3.Shantou Branch, China Mobile Group Guangdong Co., Ltd., Shantou, Guangdong 515041, China
  • Online:2021-04-15 Published:2021-04-23



  1. 1.湖北工业大学 太阳能高效利用及储能运行控制湖北省重点实验室,武汉 430068
    2.华中师范大学 数字媒体技术系,武汉 430079
    3.中国移动通信集团广东有限公司 汕头分公司,广东 汕头 515041


With the characteristics of data-driven learning, deep learning technology has made great achievements in the fields of natural language processing, image processing, and speech recognition. However, due to the deep learning model featured by deep networks, many parameters, high complexity and other characteristics, the decisions and intermediate processes made by the model are difficult for humans to understand. Therefore, exploring the interpretability of deep learning has become a new topic in the current artificial intelligence field. This review takes the interpretability of deep learning models as the research object and summarizes its progress. Firstly, the main interpretability methods are summarized and analyzed from four aspects:self-explanatory model, model-specific explanation, model-agnostic explanation, and causal interpretability. At the same time, it enumerates the application of interpretability related technologies, and finally discusses the existing problems of current interpretability research to promote the further development of the deep learning interpretability research framework.

Key words: deep learning, interpretability, artificial intelligence, causal interpretability, self-explanatory



关键词: 深度学习, 可解释性, 人工智能, 因果可解释, 自解释