卷积神经网络的可解释性研究综述

神经网络模型性能日益强大,被广泛应用于解决各类计算机相关任务,并表现出非常优秀的能力,但人类对神经网络模型的运行机制却并不完全理解.针对神经网络可解释性的研究进行了梳理和汇总,就模型可解释性研究的定义、必要性、分类、评估等方面进行了详细的讨论.从解释算法的关注点出发,提出一种神经网络可解释算法的新型分类方法,为理解神经网络提供一个全新的视角.根据提出的新型分类方法对当前卷积神经网络的可解释方法进行梳理,并对不同类别解释算法的特点进行分析和比较.同时,介绍了常见可解释算法的评估原则和评估方法.对可解释神经网络的研究方向与应用进行概述.就可解释神经网络面临的挑战进行阐述,并针对这些挑战给出可能的解决方向.

Survey on Convolutional Neural Network Interpretability

With the increasingly powerful performance of neural network models, they are widely used to solve various computer-related tasks and show excellent capabilities. However, a clear understanding of the operation mechanism of neural network models is lacking. Therefore, this study reviews and summarizes the current research on the interpretability of neural networks. A detailed discussion is rendered on the definition, necessity, classification, and evaluation of research on model interpretability. With the emphasis on the focus of interpretable algorithms, a new classification method for the interpretable algorithms of neural networks is proposed, which provides a novel perspective for the understanding of neural networks. According to the proposed method, this study sorts out the current interpretable methods for convolutional neural networks and comparatively analyzes the characteristics of interpretable algorithms falling within different categories. Moreover, it introduces the evaluation principles and methods of common interpretable algorithms and expounds on the research directions and applications of interpretable neural networks. Finally, the problems confronted in this regard are discussed, and possible solutions to these problems are given.