Two-Stage Holistic and Contrastive Explanation of Image Classification
Weiyan Xie Xiao-Hui Li Zhi Lin Leonard K. M. Poon Caleb Chen Cao Nevin L. Zhang
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.