Multi-level Reliable Guidance for Unpaired Multi-view Clustering
School of Mathematical Sciences, Nanjing Normal University（南京师范大学数学学院）；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（沃林戈大学计算与信息科技学院）
In this thesis, we address the challenging problem of unpaired multi-view clustering (UMC), which aims to achieve effective joint clustering using unpaired samples observed across multiple views. Traditional incomplete multi-view clustering (IMC) methods typically rely on paired samples to capture complementary information between views. However, such strategies become impractical in the UMC due to the absence of paired samples. Although some researchers have attempted to address this issue by preserving consistent cluster structures across views, effectively mining such consistency remains challenging when the cluster structures {with low confidence}. Therefore, we propose a novel method, Multi-level Reliable Guidance for UMC (MRG-UMC), which integrates multi-level clustering and reliable view guidance to learn consistent and confident cluster structures from three perspectives. Specifically, inner-view multi-level clustering exploits high-confidence sample pairs across different levels to reduce the impact of boundary samples, resulting in more confident cluster structures. Synthesized-view alignment leverages a synthesized-view to mitigate cross-view discrepancies and promote consistency. Cross-view guidance employs a reliable view guidance strategy to enhance the clustering confidence of poorly clustered views. These three modules are jointly optimized across multiple levels to achieve consistent and confident cluster structures. Furthermore, theoretical analyses verify the effectiveness of MRG-UMC in enhancing clustering confidence. Extensive experimental results show that MRG-UMC outperforms state-of-the-art UMC methods, achieving an average NMI improvement of 12.95\% on multi-view datasets. {The source code is available at: https://anonymous.4open.science/r/MRG-UMC-5E20.
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）
HQG-Net: Unpaired Medical Image Enhancement with High-Quality Guidance
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；NEC Laboratories America Inc.（NEC美国实验室）；Norwegian University of Science and Technology（挪威科技大学）；ETH Zürich（苏黎世联邦理工学院）；Peng Cheng Laboratory（鹏城实验室）
Unpaired Object-Level SAR-to-Optical Image Translation for Aircraft with Keypoints-Guided Diffusion Models
School of Information Science and Technology, Fudan University（复旦大学信息科学与技术学院）；Key Laboratory for Information Science of Electromagnetic Waves (Ministry of Education)（电磁波信息科学教育部重点实验室）
Reducing Bias and Variance: Generative Semantic Guidance and Bi-Layer Ensemble for Image Clustering
Institute of Big Data Science and Industry（大数据科学与产业研究院）；Key Laboratory of Evolutionary Science Intelligence of Shanxi Province（山西省进化智能科学重点实验室）；School of Artificial Intelligence, Shanxi University（山西大学人工智能学院）
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）
Hierarchical Data Generator based on Tree-Structured Stick Breaking Process for Benchmarking Clustering Methods
Wrocław University of Science and Technology（弗罗茨瓦夫理工大学）；Complexica Pty Ltd.（Complexica私人有限公司）；Institute of Computer Science, Polish Academy of Sciences（波兰科学院计算机科学研究所）；Polish-Japanese Academy of Information Technology（波日信息技术学院）
Cross-Scenario Deraining Adaptation with Unpaired Data: Superpixel Structural Priors and Multi-Stage Pseudo-Rain Synthesis
School of Information Engineering, Guangdong University of Technology（广东工业大学信息工程学院）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）
CGFformer: Cluster-Guidance Frequency Transformer for Pansharpening
College of Artificial Intelligence, Xi’an Jiaotong University（西安交通大学人工智能学院）；School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学学院）；School of Computer Science and Technology, Xi’an Jiaotong University（西安交通大学计算机科学与技术学院）
