  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. Most existing DCMVC algorithms focus on exploring the
consistency information for the deep semantic features, while ignoring the
diverse information on shallow features. To fill this gap, we propose a novel
multi-view clustering network termed CodingNet to explore the diverse and
consistent information simultaneously in this paper. Specifically, instead of
utilizing the conventional auto-encoder, we design an asymmetric structure
network to extract shallow and deep features separately. Then, by aligning the
similarity matrix on the shallow feature to the zero matrix, we ensure the
diversity for the shallow features, thus offering a better description of
multi-view data. Moreover, we propose a dual contrastive mechanism that
maintains consistency for deep features at both view-feature and pseudo-label
levels. Our framework's efficacy is validated through extensive experiments on
six widely used benchmark datasets, outperforming most state-of-the-art
multi-view clustering algorithms.
