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In unsupervised scenarios, deep contrastive multi-view clustering (DCMVC) isbecoming a hot research spot, which aims to mine the potential relationshipsbetween different views. Most existing DCMVC algorithms focus on exploring theconsistency information for the deep semantic features, while ignoring thediverse information on shallow features. To fill this gap, we propose a novelmulti-view clustering network termed CodingNet to explore the diverse andconsistent information simultaneously in this paper. Specifically, instead ofutilizing the conventional auto-encoder, we design an asymmetric structurenetwork to extract shallow and deep features separately. Then, by aligning thesimilarity matrix on the shallow feature to the zero matrix, we ensure thediversity for the shallow features, thus offering a better description ofmulti-view data. Moreover, we propose a dual contrastive mechanism thatmaintains consistency for deep features at both view-feature and pseudo-labellevels. Our framework's efficacy is validated through extensive experiments onsix widely used benchmark datasets, outperforming most state-of-the-artmulti-view clustering algorithms.

Asymmetric double-winged multi-view clustering network for exploring Diverse and Consistent Information