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.

翻译：在无监督场景下，深度对比多视图聚类（DCMVC）正成为研究热点，旨在挖掘不同视图间的潜在关系。现有大多数DCMVC算法专注于探索深度语义特征的一致性信息，却忽略了浅层特征的多样性信息。为填补这一空白，本文提出一种名为CodingNet的新型多视图聚类网络，以实现对多样性与一致性信息的同步探索。具体而言，我们摒弃传统自编码器结构，设计非对称网络分别提取浅层与深层特征；随后通过将浅层特征相似度矩阵对齐至零矩阵，确保浅层特征的多样性，从而更全面地描述多视图数据。此外，我们提出双对比机制，在视图-特征与伪标签两个层面上维持深度特征的一致性。基于六个广泛使用的基准数据集的实验验证了本框架的有效性，其性能超越多数先进多视图聚类算法。