Concept bottleneck models (CBM) are a popular way of creating more interpretable neural networks by having hidden layer neurons correspond to human-understandable concepts. However, existing CBMs and their variants have two crucial limitations: first, they need to collect labeled data for each of the predefined concepts, which is time consuming and labor intensive; second, the accuracy of a CBM is often significantly lower than that of a standard neural network, especially on more complex datasets. This poor performance creates a barrier for adopting CBMs in practical real world applications. Motivated by these challenges, we propose Label-free CBM which is a novel framework to transform any neural network into an interpretable CBM without labeled concept data, while retaining a high accuracy. Our Label-free CBM has many advantages, it is: scalable - we present the first CBM scaled to ImageNet, efficient - creating a CBM takes only a few hours even for very large datasets, and automated - training it for a new dataset requires minimal human effort. Our code is available at https://github.com/Trustworthy-ML-Lab/Label-free-CBM. Finally, in Appendix B we conduct a large scale user evaluation of the interpretability of our method.
Automatic Concept Extraction for Concept Bottleneck-based Video Classification
University of California Los Angeles（加州大学洛杉矶分校）；University College London（伦敦大学学院）；USC Information Sciences Institute（南加州大学信息科学研究所）；Imperial College London（帝国理工学院）；U.S. Army Research Laboratory（美国陆军研究实验室）；U.S. Air Force Research Lab（美国空军研究实验室）
CI-CBM: Class-Incremental Concept Bottleneck Model for Interpretable Continual Learning
Department of Electrical and Computer Engineering, University of California San Diego（加州大学圣地亚哥分校电气与计算机工程系）；Department of Computer Science and Engineering, University of California San Diego（加州大学圣地亚哥分校计算机科学与工程系）；Halıcıoğlu Data Science Institute, University of California San Diego（加州大学圣地亚哥分校Halıcıoğlu数据科学研究所）
Learning New Concepts, Remembering the Old: Continual Learning for Multimodal Concept Bottleneck Models
HKUST(GZ) Deep Interdisciplinary Intelligence Lab（香港科技大学（广州）深度跨学科智能实验室）；Wuhan University（武汉大学）；Shandong University（山东大学）；Tongji University（同济大学）；Peking University（北京大学）；University of Macau（澳门大学）；HKUST(GZ) Institute of Deep Perception Technology, JITRI Deep Interdisciplinary Intelligence Lab（香港科技大学（广州）感知技术研究所，JITRI深度跨学科智能实验室）
Language Guided Concept Bottleneck Models for Interpretable Continual Learning
School of Computer Science and Engineering, Tianjin University of Technology（天津理工大学计算机科学与工程学院）；School of Information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学技术学院）；Institute of Automation, University of Chinese Academy of Sciences（中国科学院自动化研究所）
Computer Science Department, TU Darmstadt（图宾根大学计算机科学系）；Hessian Center for AI (hessian.AI)（海德堡人工智能中心）；German Research Center for AI (DFKI)（德国人工智能研究中心）；Centre for Cognitive Science, TU Darmstadt（图宾根大学认知科学中心）
TRACE: A Concept Bottleneck Model for Longitudinal 3D Glioblastoma Response Assessment
Department of Biomedical and Healthcare Data Engineering, Faculty of Engineering, Cairo University（生物医学与健康数据工程系，工程学院，开罗大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；University of Oldenburg（奥尔登堡大学）
Provable Responsible AI and Data Analytics (PRADA) Lab（可证明的负责任AI与数据分析（PRADA）实验室）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Shanghai Jiao Tong University（上海交通大学）；The University of Auckland（奥克兰大学）；RIKEN Center for Advanced Intelligence Project (AIP)（理化学研究所先进智能项目中心（AIP））；Arizona State University（亚利桑那州立大学）；Stony Brook University（石溪大学）
University of Virginia（弗吉尼亚大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）；University of Georgia（佐治亚大学）；Shanghai Jiao Tong University（上海交通大学）；HKUST（香港科技大学）；Ant Group（蚂蚁集团）
Department of Applied Mathematics & Statistics（应用数学与统计学系）；State University of New York at Stony Brook（石溪州立大学）；Halıcıoğlu Data Science Institute（Halıcıoğlu数据科学研究所）；University of California, San Diego（加州大学圣地亚哥分校）；Thomas J. Watson Research Center（汤普森·J·沃森研究中心）；IBM Research（IBM研究院）；Department of Biomedical Informatics（生物医学信息学系）
Interpretable Reward Modeling with Active Concept Bottlenecks
Department of Computer Science, ETH Zurich, Zurich, Switzerland（苏黎世联邦理工学院计算机科学系）；Department of Applied Mathematics and Theoretical Physics, University of Cambridge, United Kingdom（剑桥大学应用数学与理论物理系）
