Post-hoc explainability methods such as Grad-CAM are popular because they do not influence the performance of a trained model. However, they mainly reveal "where" a model looks at for a given input, fail to explain "what" the model looks for (e.g., what is important to classify a bird image to a Scott Oriole?). Existing part-prototype networks leverage part-prototypes (e.g., characteristic Scott Oriole's wing and head) to answer both "where" and "what", but often under-perform their black box counterparts in the accuracy. Therefore, a natural question is: can one construct a network that answers both "where" and "what" in a post-hoc manner to guarantee the model's performance? To this end, we propose the first post-hoc part-prototype network via decomposing the classification head of a trained model into a set of interpretable part-prototypes. Concretely, we propose an unsupervised prototype discovery and refining strategy to obtain prototypes that can precisely reconstruct the classification head, yet being interpretable. Besides guaranteeing the performance, we show that our network offers more faithful explanations qualitatively and yields even better part-prototypes quantitatively than prior part-prototype networks.
Post-hoc Overall Survival Time Prediction from Brain MRI
Australian Institute for Machine Learning, The University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；Institute for Systems and Robotics, Instituto Superior Tecnico（高等理工学院系统与机器人研究所）
Supervised Classification Heads as Semantic Prototypes: Unlocking Vision-Language Alignment via Weight Recycling
Department of Computer Science and Artificial Intelligence, DaSCI Institute, University of Granada, Granada, Spain（计算机科学与人工智能系，DaSCI研究所，格拉纳达大学，格拉纳达，西班牙）；Department of Mathematics ``Tullio Levi-Civita'', University of Padova, Padova, Italy（托里利-西维塔数学系，帕多瓦大学，帕多瓦，意大利）
Deep Attention Reweighting: Post-Hoc Attention-Based Feature Aggregation in CNNs for Disentangling Core and Spurious Features under Spurious Correlations
Improved post-hoc probability calibration for out-of-domain MRI segmentation
Imperial College London（帝国理工学院）；Fudan University（复旦大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Technical University of Munich（慕尼黑工业大学）
Evaluating Post-hoc Interpretability with Intrinsic Interpretability
University of Coimbra（科英布拉大学）；IPO-Porto Research Centre（波尔图肿瘤学研究所研究中心）；University of Applied Sciences Western Switzerland (HES-SO Valais)（瑞士西部应用科学大学（瓦莱州高等专业学院））
On the Faithfulness of Post-Hoc Concept Bottleneck Models
Institute of Data Science, German Aerospace Center（数据科学研究所，德国航空航天中心）；Computer Vision Group Jena, Friedrich Schiller University Jena（贾恩计算机视觉小组，弗里德里希·席勒大学贾恩）；GEOMAR Helmholtz Centre for Ocean Research Kiel（基尔海洋研究赫尔姆霍茨中心）
Train Once, Forget Precisely: Anchored Optimization for Efficient Post-Hoc Unlearning
University College London, London, United Kingdom（伦敦大学学院）；University of Nottingham, Nottingham, United Kingdom（诺丁汉大学）；Bharati Vidyapeeth College of Engineering, New Delhi, India（巴里蒂大学工程学院）
Technical University of Munich（慕尼黑工业大学）；Helmholtz Munich（亥姆霍兹慕尼黑中心）；ELLIS Institute Finland（芬兰ELLIS研究所）；Aalto University（阿尔托大学）；Finnish Center for Artificial Intelligence（芬兰人工智能中心）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Munich Data Science Institute（慕尼黑数据科学研究所）；Ericsson Research（爱立信研究院）；KTH Royal Institute of Technology（KTH皇家理工学院）；Digital Futures（数字未来研究所）
