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/.

翻译：解释和说明深度神经网络的行为对许多任务至关重要。可解释人工智能提供了一种应对这一挑战的方法，主要通过为决策提供逐像素的相关性。然而，理解这些解释可能需要专业知识。近期一些提升可解释性的尝试采用了基于概念的框架，从而建立某些概念与模型决策之间的高层次关系。本文提出了瓶颈概念学习器（Bottleneck Concept Learner, BotCL），它仅通过目标任务训练过程中学习到的概念存在与否来表示图像，而无需对这些概念进行显式监督。该方法利用自监督学习和专门设计的正则化器，使所学概念能够被人类理解。以若干图像分类任务作为测试平台，我们展示了BotCL在重构神经网络以提升可解释性方面的潜力。代码见https://github.com/wbw520/BotCL，简易演示见https://botcl.liangzhili.com/。