Explainable Convolutional Neural Networks: A Taxonomy, Review, and Future Directions

被引:25
|
作者
Ibrahim, Rami [1 ]
Shafiq, M. Omair [1 ]
机构
[1] Carleton Univ, Sch Informat Technol, 1125 Colonel By Dr, Ottawa, ON K1S 5B6, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Explainable AI; convolutional neural networks; Interpretable AI; survey; DEEP; INTERPRETABILITY; DEFENSE;
D O I
10.1145/3563691
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Convolutional neural networks (CNNs) have shown promising results and have outperformed classical machine learning techniques in tasks such as image classification and object recognition. Their human-brain like structure enabled them to learn sophisticated features while passing images through their layers. However, their lack of explainability led to the demand for interpretations to justify their predictions. Research on Explainable AI or XAI has gained momentum to provide knowledge and insights into neural networks. This study summarizes the literature to gain more understanding of explainability in CNNs (i.e., Explainable Convolutional Neural Networks). We classify models that made efforts to improve the CNNs interpretation. We present and discuss taxonomies for XAI models that modify CNN architecture, simplify CNN representations, analyze feature relevance, and visualize interpretations. We review various metrics used to evaluate XAI interpretations. In addition, we discuss the applications and tasks of XAImodels. This focused and extensive survey develops a perspective on this area by addressing suggestions for overcoming XAI interpretation challenges, like models' generalization, unifying evaluation criteria, building robust models, and providing interpretations with semantic descriptions. Our taxonomy can be a reference to motivate future research in interpreting neural networks.
引用
收藏
页数:37
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