重新思考概念瓶颈模型：从误区到解决方案
Rethinking Concept Bottleneck Models: From Pitfalls to Solutions
AI总结：
CBM-Suite通过引入熵度量、非线性层和蒸馏损失，系统解决概念瓶颈模型的准确性、可解释性和系统性研究问题。
摘要：
概念瓶颈模型（CBMs）将预测基于人类可理解的概念，但面临根本性限制：缺乏评估概念相关性的度量标准，
英文摘要：
Concept Bottleneck Models (CBMs) ground predictions in human-understandable concepts but face fundamental limitations: the absence of a metric to pre-evaluate concept relevance, the "linearity problem" causing recent CBMs to bypass the concept bottleneck entirely, an accuracy gap compared to opaque models, and finally the lack of systematic study on the impact of different visual backbones and VLMs. We introduce CBM-Suite, a methodological framework to systematically addresses these challenges. First, we propose an entropy-based metric to quantify the intrinsic suitability of a concept set for a given dataset. Second, we resolve the linearity problem by inserting a non-linear layer between concept activations and the classifier, which ensures that model accuracy faithfully reflects concept relevance. Third, we narrow the accuracy gap by leveraging a distillation loss guided by a linear teacher probe. Finally, we provide comprehensive analyses on how different vision encoders, vision-language models, and concept sets interact to influence accuracy and interpretability in CBMs. Extensive evaluations show that CBM-Suite yields more accurate models and provides insights for improving concept-based interpretability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
