面向目标用户的深度学习模型可视化综述  

Review on Visualization of Deep Learning Models for Target Users

作　　者：胡凯茜 李欣[1] 裴炳森 HU Kai-Xi;LI Xin;PEI Bing-Sen(Academy of Information Network Security,People’s Public Security University of China,Beijing 100038,China)

机构地区：[1]中国人民公安大学信息网络安全学院,北京100038

出　　处：《计算机系统应用》2023年第11期36-47,共12页Computer Systems & Applications

基　　金：国家重点研发计划(2020AAA0107705)。

摘　　要：深度学习模型在某些场景的实际应用中要求其具备一定的可解释性,而视觉是人类认识周围世界的基本工具,可视化技术能够将模型训练过程从不可见的黑盒状态转换为可交互分析的视觉过程,从而有效提高模型的可信性和可解释度.目前,国内外相关领域缺少有关深度学习模型可视化工具的综述,也缺乏对不同用户实际需求的研究和使用体验的评估.因此,本文通过调研近年来学术界模型可解释性和可视化相关文献,总结可视化工具在不同领域的应用现状,提出面向目标用户的可视化工具分类方法及依据,对每一类工具从可视化内容、计算成本等方面进行介绍和对比,以便不同用户选取与部署合适的工具.最后在此基础上讨论可视化领域存在的问题并加以展望.Deep learning models require certain interpretability in practical applications in certain scenarios,and vision is a basic tool for humans to understand the surrounding world.Visualization technology can transform the model training process from an invisible black box to an interactive and analyzable visual process,effectively improving the credibility and interpretability of the model.At present,there is a lack of review on deep learning model visualization tools in related fields,as well as a lack of research on the actual needs of different users and the evaluation of user experience.Therefore,this study summarizes the current situation of the application of visualization tools in different fields by investigating the literature related to interpretability and visualization in recent years.It proposes a classification method and basis for target user-oriented visualization tools and introduces and compares each type of tool from the aspects of visualization content,computational cost,etc.,so that different users can select and deploy suitable tools.Finally,on this basis,the problems in the field of visualization are discussed and its prospects are provided.

关 键 词：可视化工具 深度学习模型 模型可解释性 目标用户 注意力机制 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]