深度学习中的视觉可解释性

张拳石; 朱松纯

doi:10.1631/FITEE.1700808

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深度学习中的视觉可解释性

2018 Special Issue on Artificial Intelligence 2.0: Theories and Applications | Updated：2022-05-19

- 深度学习中的视觉可解释性
- Visual interpretability for deep learning: a survey
- 信息与电子工程前沿（英文） 2018年19卷第1期页码：27-39
- Affiliations：
  
  University of California, Los Angeles, California 90095, USA
- Author bio：
  
  Quan-shi ZHANG, E-mail: zhangqs@ucla.edu
  [ "Song-chun ZHU,E-mail: sczhu@stat.ucla.edu" ]
- Funds：
  
  Project supported by the ONR MURI project (No. N00014-16-1-2007), the DARPA XAI Award (No. N66001-17-2-4029), and NSF IIS (No. 1423305)
- DOI：10.1631/FITEE.1700808
  中图分类号： TP391
- 收稿：2017-12-02，
  
  修回：2018-;1-28，
  
  纸质出版：2018-01
- Accepted：
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张拳石, 朱松纯. 深度学习中的视觉可解释性[J]. 信息与电子工程前沿（英文）, 2018,19(1):27-39.

Quan-shi ZHANG, Song-chun ZHU. Visual interpretability for deep learning: a survey[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(1): 27-39.
张拳石, 朱松纯. 深度学习中的视觉可解释性[J]. 信息与电子工程前沿（英文）, 2018,19(1):27-39. DOI： 10.1631/FITEE.1700808.

Quan-shi ZHANG, Song-chun ZHU. Visual interpretability for deep learning: a survey[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(1): 27-39. DOI： 10.1631/FITEE.1700808.

摘要

总结了近年来在理解神经网络内部特征表达和训练一个具有中层表达可解释性的深度神经网络上的相关研究工作。虽然深度神经网络在众多人工智能任务中已有杰出表现，但神经网络中层表达的可解释性依然是该领域发展的重大瓶颈。目前，深度神经网络以低解释性的黑箱表达为代价，获取了强大的分类能力。我们认为提高神经网络中层特征表达的可解释性，可以帮助人们打破众多深度学习的发展瓶颈，比如，小数据训练，语义层面上的人机交互式训练，以及基于内在特征语义定向精准修复网络中层特征表达缺陷等难题。本文着眼于卷积神经网络，调研了：(1) 网络表达可视化方法；(2) 网络表达的诊断方法；(3) 自动解构解释卷积神经网络的方法；(4) 学习中层特征表达可解释的神经网络的方法；(5) 基于网络可解释性的中层对端的深度学习算法。最后，讨论了可解释性人工智能未来可能的发展趋势。

Abstract

This paper reviews recent studies in understanding neural-network representations and learning neural networks with interpretable/disentangled middle-layer representations. Although deep neural networks have exhibited superior performance in various tasks

interpretability is always Achilles' heel of deep neural networks. At present

deep neural networks obtain high discrimination power at the cost of a low interpretability of their black-box representations. We believe that high model interpretability may help people break several bottlenecks of deep learning

e.g.

learning from a few annotations

learning via human-computer communications at the semantic level

and semantically debugging network representations. We focus on convolutional neural networks (CNNs)

and revisit the visualization of CNN representations

methods of diagnosing representations of pre-trained CNNs

approaches for disentangling pre-trained CNN representations

learning of CNNs with disentangled representations

and middle-to-end learning based on model interpretability. Finally

we discuss prospective trends in explainable artificial intelligence.

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