Title: CRRC: Residual Cross-view Learning for Deep Multi-view Clustering
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Deep Multi-View Clustering, Contrastive Learning, Residual Connections, Dynamic Gating, Attention-Based Weighting

Abstract: Deep multi-view clustering aims to integrate complementary information from multiple heterogeneous views to improve clustering performance.  However, existing fusion strategies often struggle to balance shared semantics and view-specific heterogeneity, as they typically rely on direct concatenation or rigid alignment, which obscures subtle cross-view patterns and assumes equal contribution from all views.  To address these limitations, we propose CRRC, a novel framework that leverages residual connections to recalibrate view-specific features by adaptively incorporating complementary information from other views.  Specifically, CRRC introduces a dynamic gating fusion module to control residual flow based on view characteristics, and an attention-based weighting mechanism to emphasize semantically relevant cross-view signals.  These components work collaboratively to enhance feature discriminability and consistency.  Extensive experiments on benchmarks datasets demonstrate that CRRC outperforms state-of-the-art methods in accuracy, NMI, and purity, validating its effectiveness in achieving robust multi-view clustering.

TL;DR: CRRC