Deep Multiview Clustering by Contrasting Cluster Assignments

Jie Chen, Hua Mao, Wai Lok Woo, Xi Peng; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 16752-16761

Abstract


Multiview clustering (MVC) aims to reveal the underlying structure of multiview data by categorizing data samples into clusters. Deep learning-based methods exhibit strong feature learning capabilities on large-scale datasets. For most existing deep MVC methods, exploring the invariant representations of multiple views is still an intractable problem. In this paper, we propose a cross-view contrastive learning (CVCL) method that learns view-invariant representations and produces clustering results by contrasting the cluster assignments among multiple views. Specifically, we first employ deep autoencoders to extract view-dependent features in the pretraining stage. Then, a cluster-level CVCL strategy is presented to explore consistent semantic label information among the multiple views in the fine-tuning stage. Thus, the proposed CVCL method is able to produce more discriminative cluster assignments by virtue of this learning strategy. Moreover, we provide a theoretical analysis of soft cluster assignment alignment. Extensive experimental results obtained on several datasets demonstrate that the proposed CVCL method outperforms several state-of-the-art approaches.

Related Material


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[bibtex]
@InProceedings{Chen_2023_ICCV, author = {Chen, Jie and Mao, Hua and Woo, Wai Lok and Peng, Xi}, title = {Deep Multiview Clustering by Contrasting Cluster Assignments}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2023}, pages = {16752-16761} }