Two-Stage Holistic and Contrastive Explanation of Image Classification

• URL: http://arxiv.org/abs/2306.06339v1
• Date: Sat, 10 Jun 2023 04:22:13 GMT
• Title: Two-Stage Holistic and Contrastive Explanation of Image Classification
• Authors: Weiyan Xie, Xiao-Hui Li, Zhi Lin, Leonard K. M. Poon, Caleb Chen Cao, Nevin L. Zhang
• Abstract summary: A whole-output explanation can help a human user gain an overall understanding of model behaviour. We propose a contrastive whole-output explanation (CWOX) method for image classification.
• Score: 16.303752364521454
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The need to explain the output of a deep neural network classifier is now widely recognized. While previous methods typically explain a single class in the output, we advocate explaining the whole output, which is a probability distribution over multiple classes. A whole-output explanation can help a human user gain an overall understanding of model behaviour instead of only one aspect of it. It can also provide a natural framework where one can examine the evidence used to discriminate between competing classes, and thereby obtain contrastive explanations. In this paper, we propose a contrastive whole-output explanation (CWOX) method for image classification, and evaluate it using quantitative metrics and through human subject studies. The source code of CWOX is available at https://github.com/vaynexie/CWOX.

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