Out-of-distribution (OOD) detection aims to detect testing samples far away from the in-distribution (ID) training data, which is crucial for the safe deployment of machine learning models in the real world. Distance-based OOD detection methods have emerged with enhanced deep representation learning. They identify unseen OOD samples by measuring their distances from ID class centroids or prototypes. However, existing approaches learn the representation relying on oversimplified data assumptions, e.g, modeling ID data of each class with one centroid class prototype or using loss functions not designed for OOD detection, which overlook the natural diversities within the data. Naively enforcing data samples of each class to be compact around only one prototype leads to inadequate modeling of realistic data and limited performance. To tackle these issues, we propose PrototypicAl Learning with a Mixture of prototypes (PALM) which models each class with multiple prototypes to capture the sample diversities, and learns more faithful and compact samples embeddings to enhance OOD detection. Our method automatically identifies and dynamically updates prototypes, assigning each sample to a subset of prototypes via reciprocal neighbor soft assignment weights. PALM optimizes a maximum likelihood estimation (MLE) loss to encourage the sample embeddings to be compact around the associated prototypes, as well as a contrastive loss on all prototypes to enhance intra-class compactness and inter-class discrimination at the prototype level. Moreover, the automatic estimation of prototypes enables our approach to be extended to the challenging OOD detection task with unlabelled ID data. Extensive experiments demonstrate the superiority of PALM, achieving state-of-the-art average AUROC performance of 93.82 on the challenging CIFAR-100 benchmark. Code is available at https://github.com/jeff024/PALM.
Mixture of Gaussian-distributed Prototypes with Generative Modelling for Interpretable and Trustworthy Image Recognition
Australian Institute for Machine Learning (AIML), University of Adelaide（阿德莱德大学澳大利亚机器学习研究所）；Stanford University（斯坦福大学）；Cornell University（康奈尔大学）；Cornell Tech（康奈尔理工学院）；University of Oxford（牛津大学）；St. Vincent’s Institute of Medical Research（圣文森特医学研究所）；Melbourne Integrative Genomics, The University of Melbourne（墨尔本大学墨尔本整合基因组学中心）；St Vincent’s Hospital Melbourne（墨尔本圣文森特医院）；Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey（萨里大学视觉、语音与信号处理中心）；The University of Adelaide（阿德莱德大学）
How to Achieve Prototypical Birth and Death for OOD Detection?
School of Computer and Electronic Information, Nanjing Normal University（南京师范大学计算机与电子信息学院）；School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine（南京中医药大学人工智能与信息技术学院）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University, Shenzhen（中山大学深圳校区网络安全科学与技术学院）；Tohoku University（东北大学）
Prototype-based Optimal Transport for Out-of-Distribution Detection
School of Biomedical Engineering, University of Science and Technology of China（中国科学技术大学生物医学工程学院）；Data Darkness Lab, MIRACLE Center, Suzhou Institute for Advanced Research, USTC（中国科学技术大学苏州高等研究院MIRACLE中心Data Darkness实验室）
Pre-training Epidemic Time Series Forecasters with Compartmental Prototypes
Emory University Department of Computer Science Atlanta GA USA（埃默里大学计算机科学系）；Emory University Rollins School of Public Health Atlanta GA USA（埃默里大学罗林斯公共卫生学院）；Emory University（埃默里大学）
Mila - Quebec AI Institute（魁北克AI研究所）；DIRO, Université de Montréal（蒙特利尔大学计算研究所）；Université Laval（拉瓦尔大学）；IID（智能与数据研究所）；DEEL, IRT Saint Exupéry（DEEL，圣埃克苏佩里研究所）；CIFAR AI Chair（CIFAR人工智能席位）
DynProto: Dynamic Prototype Evolution for Out-of-Distribution Detection
Sun Yat-sen University（中山大学）；Peng Cheng Laboratory（鹏城实验室）；University of Nottingham Malaysia（诺丁汉马来西亚大学）；Hong Kong University of Science and Technology（香港科学与技术大学）；Key Laboratory of Machine Intelligence and Advanced Computing（智能与先进计算关键实验室）
Brain-inspired analogical mixture prototypes for few-shot class-incremental learning
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences（中国科学院香港创新研究院）
Pro-t-VAE: Closed-Form Student's t Mixtures for Robust Prototype Discovery
Institute of Intelligent Information Processing, Shanxi University（山西大学智能信息处理研究所）；School of Medicine, Yale University（耶鲁大学医学院）；Boston Children’s Hospital, Harvard Medical School（哈佛医学院波士顿儿童医院）；Eli Lilly and Company（礼来公司）
Interpretable Graph-Level Anomaly Detection via Contrast with Normal Prototypes
State Key Laboratory for Novel Software Technology, Nanjing University（新型软件技术国家重点实验室，南京大学）；School of Artificial Intelligence, Nanjing University（人工智能学院，南京大学）
Multi-Normal Prototypes Learning for Weakly Supervised Anomaly Detection
School of Software and Microelectronics Peking University（北京大学软件与微电子学院）；School of Computer Science Peking University（北京大学计算机学院）；National Engineering Center of Software Engineering Peking University（北京大学软件工程国家工程研究中心）
EVA-Net: Interpretable Anomaly Detection for Brain Health via Learning Continuous Aging Prototypes from One-Class EEG Cohorts
Qilu Hospital, ASU（青岛医院，亚利桑那州立大学）；Zhengzhou University（郑州大学）；University of Science and Technology Beijing（北京科技大学）；Southern University of Science and Technology（南方科技大学）；Qilu Hospital（青岛医院）
Generalist Graph Anomaly Detection via Prototype-Based Distillation
School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China（西安交通大学计算机科学与技术学院）；National Engineering Research Center for Visual Information and Applications, Xi'an, China（视觉信息与应用国家工程研究中心）；University of Virginia, Charlottesville, USA（弗吉尼亚大学）；University of Central Florida, Orlando, USA（佛罗里达大学）；School of Distance Education, Xi’an Jiaotong University, Xi'an, China（西安交通大学继续教育学院）；School of Cyber Science and Engineering, Xi'an Jiaotong University, Xi'an, China（西安交通大学网络安全学院）
PAST-TIDE: Prototype-Anchored Statement Tuning with Topic-Invariant Normalization for Stance Detection
Department of Computer Science and Engineering, Khulna University of Engineering & Technology（库尔纳工程技术大学计算机科学与工程系）；Department of Electrical and Computer Engineering, University of Waterloo（滑铁卢大学电气与计算机工程系）；Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学机器学习系）
