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Grounding Counterfactual Explanation of Image Classifiers to Textual Concept Space

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Authors

Kim, Siwon; Oh, Jinoh; Lee, Sung Jin; Yu, Seung Hak; Do, Jae Young; Taghavi, Tara

Issue Date
2023-06
Publisher
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Citation
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.10942-10950
Abstract
Concept-based explanation aims to provide concise and human-understandable explanations of an image classifier. However, existing concept-based explanation methods typically require a significant amount of manually collected concept-annotated images. This is costly and runs the risk of human biases being involved in the explanation. In this paper, we propose Counterfactual explanation with text-driven concepts (CounTEX), where the concepts are defined only from text by leveraging a pre-trained multimodal joint embedding space without additional concept-annotated datasets. A conceptual counterfactual explanation is generated with text-driven concepts. To utilize the text-driven concepts defined in the joint embedding space to interpret target classifier outcome, we present a novel projection scheme for mapping the two spaces with a simple yet effective implementation. We show that CounTEX generates faithful explanations that provide a semantic understanding of model decision rationale robust to human bias.
ISSN
1063-6919
URI
https://hdl.handle.net/10371/201360
DOI
https://doi.org/10.1109/CVPR52729.2023.01053
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  • College of Engineering
  • Department of Electrical and Computer Engineering
Research Area AI 애플리케이션을 위한 알고리즘-시스템 공동 설계, AI-powered Big Data Management, Generative AI, Large Language Model, ML, 고성능 대규모 AI 데이터 분석 및 처리, 모달 AI

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