深度学习的可解释性研究:文献调查

IF 14.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Biao Xu, Guanci Yang
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引用次数: 0

摘要

深度学习(DL)已被广泛应用于各个领域。然而,其黑箱性质限制了人们对其决策过程的理解和信任。因此,研究深度学习的可解释性变得至关重要,它可以阐明模型的决策过程和行为。本综述概述了可解释性研究的现状。首先,介绍了 DL 的典型模型、原理和应用。然后,阐明了可解释性的定义和意义。随后,介绍了一些典型的可解释性算法,分为四类:主动解释、被动解释、补充解释和综合解释。之后,简要介绍了几种可解释性评价指标,并探讨了可解释性与模型性能之间的关系。接着,介绍了一些可解释性方法/模型在实际场景中的具体应用。最后,讨论了可解释性研究的挑战和未来发展方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Interpretability research of deep learning: A literature survey
Deep learning (DL) has been widely used in various fields. However, its black-box nature limits people's understanding and trust in its decision-making process. Therefore, it becomes crucial to research the DL interpretability, which can elucidate the model's decision-making processes and behaviors. This review provides an overview of the current status of interpretability research. First, the DL's typical models, principles, and applications are introduced. Then, the definition and significance of interpretability are clarified. Subsequently, some typical interpretability algorithms are introduced into four groups: active, passive, supplementary, and integrated explanations. After that, several evaluation indicators for interpretability are briefly described, and the relationship between interpretability and model performance is explored. Next, the specific applications of some interpretability methods/models in actual scenarios are introduced. Finally, the interpretability research challenges and future development directions are discussed.
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来源期刊
Information Fusion
Information Fusion 工程技术-计算机:理论方法
CiteScore
33.20
自引率
4.30%
发文量
161
审稿时长
7.9 months
期刊介绍: Information Fusion serves as a central platform for showcasing advancements in multi-sensor, multi-source, multi-process information fusion, fostering collaboration among diverse disciplines driving its progress. It is the leading outlet for sharing research and development in this field, focusing on architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome.
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