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arXiv · 2304.10131

Learning Bottleneck Concepts in Image Classification

Bowen Wang·Liangzhi Li·Yuta Nakashima·Hajime Nagahara
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Abstract

Interpreting and explaining the behavior of deep neural networks is critical for many tasks. Explainable AI provides a way to address this challenge, mostly by providing per-pixel relevance to the decision. Yet, interpreting such explanations may require expert knowledge. Some recent attempts toward interpretability adopt a concept-based framework, giving a higher-level relationship between some concepts and model decisions. This paper proposes Bottleneck Concept Learner (BotCL), which represents an image solely by the presence/absence of concepts learned through training over the target task without explicit supervision over the concepts. It uses self-supervision and tailored regularizers so that learned concepts can be human-understandable. Using some image classification tasks as our testbed, we demonstrate BotCL's potential to rebuild neural networks for better interpretability. Code is available at https://github.com/wbw520/BotCL and a simple demo is available at https://botcl.liangzhili.com/.

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Bowen Wang, Liangzhi Li, Yuta Nakashima, Hajime Nagahara. 2023-04-20. Learning Bottleneck Concepts in Image Classification. https://arxiv.org/abs/2304.10131

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