Aligning distributions of view representations is a core component of today's state of the art models for deep multi-view clustering. However, we identify several drawbacks with naïvely aligning representation distributions. We demonstrate that these drawbacks both lead to less separable clusters in the representation space, and inhibit the model's ability to prioritize views. Based on these observations, we develop a simple baseline model for deep multi-view clustering. Our baseline model avoids representation alignment altogether, while performing similar to, or better than, the current state of the art. We also expand our baseline model by adding a contrastive learning component. This introduces a selective alignment procedure that preserves the model's ability to prioritize views. Our experiments show that the contrastive learning component enhances the baseline model, improving on the current state of the art by a large margin on several datasets.
DAS-PMVC: A Framework for Partial Multi-View Clustering via Dual Alignment and Structure Enhancement
Dalian University of Technology（大连理工大学）；Inspur Group Co., Ltd.（浪潮集团有限公司）；China University of Mining and Technology（中国矿业大学）；The University of Warwick（华威大学）
Reconsidering Explicit Longitudinal Mammography Alignment for Enhanced Breast Cancer Risk Prediction
Department of Physics and Technology, UiT The Arctic University of Norway（物理与技术系，UiT北极大学）；SPKI The Norwegian Centre for Clinical Artificial Intelligence（挪威临床人工智能中心）；Department of Computer Science and Engineering, University of Bologna（计算机科学与工程系，博洛尼亚大学）；Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering IMTE（个体化和细胞基础医学工程研究机构IMTE）；Norwegian Computing Center, Oslo, Norway（挪威计算中心，奥斯陆，挪威）；Pioneer Centre for AI, University of Copenhagen（人工智能先锋中心，哥本哈根大学）
Understanding InfoNCE: Transition Probability Matrix Induced Feature Clustering
organization= School of Mathematics；Computational Science, Xiangtan University , addressline= Yuhu District , city= Xiangtan , postcode= 411105 , state= Hunan Province , country= China；organization= School of Computer Science \& School of Cyberspace Science, Xiangtan University , addressline= Yuhu District , city= Xiangtan , postcode= 411105 , state= Hunan Province , country= China；organization= Investigation Department, Hunan Police Academy , addressline= Xingsha District , city= Changsha , postcode= 410138 , state= Hunan Province , country= China
College of Computer Science & Technology, Nanjing University of Aeronautics & Astronautics（南京航空航天大学计算机科学与技术学院）；Collaborative Innovation Center of Novel Software Technology and Industrialization（新型软件技术与产业化协同创新中心）
Deep Incomplete Multi-view Clustering with Distribution Dual-Consistency Recovery Guidance
School of Computer, National University of Defense Technology（国防科技大学计算机学院）；Academy of Military Sciences（军事科学院）；Chinese PLA General Hospital（中国人民解放军总医院）
Unsupervised Visible-Infrared ReID via Pseudo-label Correction and Modality-level Alignment
Artificial Intelligence Thrust, HKUST(GZ)（香港科技大学（广州）人工智能分支）；Center for AI Research (CAIR), University of Agder(UiA)（阿格德大学人工智能研究中心）；Dept. of Computer Science and Engineering, HKUST（香港科技大学计算机科学与工程系）
Probabilistically Aligned View-unaligned Clustering with Adaptive Template Selection
School of Science, Jiangnan University（江南大学理学院）；School of Artificial Intelligence and Computer Science, Jiangnan University（江南大学人工智能与计算机学院）；Centre for Vision, Speech and Signal Processing, University of Surrey（萨里大学视觉、语音与信号处理中心）
Unpaired Multi-view Clustering via Reliable View Guidance
School of Mathematical Sciences, Nanjing Normal University（南京师范大学数学科学学院）；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 Computing and Information Technology, University of Wollongong（伍伦贡大学计算与信息技术学院）
Target Semantics Clustering via Text Representations for Robust Universal Domain Adaptation
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention
The University of Sydney（悉尼大学）；School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology（集成空天地海大数据应用技术国家工程实验室）；School of Computer Science and Engineering, Northwestern Polytechnical University（西北工业大学计算机科学与工程学院）；University of Maryland（马里兰大学）；Ningbo Institute of Northwestern Polytechnical University（西北工业大学宁波学院）
