150 citations · 382 across the 30 of their papers we have counts for
9 papers · 1 filter
Generalized Deep Multi-view Clustering via Causal Learning with Partially Aligned Cross-view Correspondence
Xihong Yang, Siwei Wang, Jiaqi Jin +6
Multi-view clustering (MVC) aims to explore the common clustering structure across multiple views. Many existing MVC methods heavily rely on the assumption of view consistency, whe…
Automatically Identify and Rectify: Robust Deep Contrastive Multi-view Clustering in Noisy Scenarios
Xihong Yang, Siwei Wang, Fangdi Wang +6
Leveraging the powerful representation learning capabilities, deep multi-view clustering methods have demonstrated reliable performance by effectively integrating multi-source info…
Imputation-free and Alignment-free: Incomplete Multi-view Clustering Driven by Consensus Semantic Learning
Yuzhuo Dai, Jiaqi Jin, Zhibin Dong +6
In incomplete multi-view clustering (IMVC), missing data induce prototype shifts within views and semantic inconsistencies across views. A feasible solution is to explore cross-vie…
Deep Incomplete Multi-view Clustering with Distribution Dual-Consistency Recovery Guidance
Jiaqi Jin, Siwei Wang, Zhibin Dong +4
Multi-view clustering leverages complementary representations from diverse sources to enhance performance. However, real-world data often suffer incomplete cases due to factors lik…
Asymmetric double-winged multi-view clustering network for exploring Diverse and Consistent Information
Qun Zheng, Xihong Yang, Siwei Wang +2
In unsupervised scenarios, deep contrastive multi-view clustering (DCMVC) is becoming a hot research spot, which aims to mine the potential relationships between different views. M…
DealMVC: Dual Contrastive Calibration for Multi-view Clustering
Xihong Yang, Jiaqi Jin, Siwei Wang +7
Benefiting from the strong view-consistent information mining capacity, multi-view contrastive clustering has attracted plenty of attention in recent years. However, we observe the…
Topological Structure Learning for Weakly-Supervised Out-of-Distribution Detection
Rundong He, Rongxue Li, Zhongyi Han +1
Out-of-distribution (OOD) detection is the key to deploying models safely in the open world. For OOD detection, collecting sufficient in-distribution (ID) labeled data is usually m…
Improved Dual Correlation Reduction Network
Yue Liu, Sihang Zhou, Xinwang Liu +2
Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different clusters without human annotations, is a fundamental yet challenging…
Interpolation-based Contrastive Learning for Few-Label Semi-Supervised Learning
Xihong Yang, Xiaochang Hu, Sihang Zhou +2
Semi-supervised learning (SSL) has long been proved to be an effective technique to construct powerful models with limited labels. In the existing literature, consistency regulariz…