AS-XAI: Self-supervised Automatic Semantic Interpretation for CNN

• URL: http://arxiv.org/abs/2312.14935v1
• Date: Sat, 2 Dec 2023 10:06:54 GMT
• Title: AS-XAI: Self-supervised Automatic Semantic Interpretation for CNN
• Authors: Changqi Sun, Hao Xu, Yuntian Chen, Dongxiao Zhang
• Abstract summary: We propose a self-supervised automatic semantic interpretable artificial intelligence (AS-XAI) framework. It utilizes transparent embedding semantic extraction spaces and row-centered principal component analysis (PCA) for global semantic interpretation of model decisions. The proposed approach offers broad fine-grained practical applications, including shared semantic interpretation under out-of-distribution categories.
• Score: 5.42467030980398
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Explainable artificial intelligence (XAI) aims to develop transparent explanatory approaches for "black-box" deep learning models. However,it remains difficult for existing methods to achieve the trade-off of the three key criteria in interpretability, namely, reliability, causality, and usability, which hinder their practical applications. In this paper, we propose a self-supervised automatic semantic interpretable explainable artificial intelligence (AS-XAI) framework, which utilizes transparent orthogonal embedding semantic extraction spaces and row-centered principal component analysis (PCA) for global semantic interpretation of model decisions in the absence of human interference, without additional computational costs. In addition, the invariance of filter feature high-rank decomposition is used to evaluate model sensitivity to different semantic concepts. Extensive experiments demonstrate that robust and orthogonal semantic spaces can be automatically extracted by AS-XAI, providing more effective global interpretability for convolutional neural networks (CNNs) and generating human-comprehensible explanations. The proposed approach offers broad fine-grained extensible practical applications, including shared semantic interpretation under out-of-distribution (OOD) categories, auxiliary explanations for species that are challenging to distinguish, and classification explanations from various perspectives.

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