mlsanityData to 14 Oct 2026

Learning Disentangled Representations for Generalized Multi-view Clustering

arXiv 2605.15640 ↗First posted 15 May 2026cs.CVcited by 2

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Abstract (arXiv)

Multi-View Clustering (MVC) has gained significant attention for its ability to leverage complementary information across diverse views. However, existing deep MVC methods often struggle with view-distribution entanglement during cross-view fusion, which hampers the quality of the shared latent space and leads to suboptimal Figures. To address this issue, we propose the Generalized Multi-view Auto-Encoder (GMAE), a framework designed to preserve cross-view complementarity through disentangled representation learning. Specifically, GMAE employs dual-path autoencoders to decouple source features into view-specific and view-common embeddings, facilitating the discovery of clearer clustering structures. We further construct cross-view adversarial discriminators to guide view-specific encoders in capturing more discriminative features. By strategically modulating mutual information, GMAE effectively aligns distributions and prevents representation collapse, ensuring the generation of robust, non-trivial embeddings. Comprehensive experiments on 13 benchmark datasets demonstrate that GMAE consistently outperforms state-of-the-art methods in both complete and incomplete MVC tasks. Our code implementation is available at the repository: https://github.com/obananas/GMAE.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 35 of its 94 references that are in the map. First posted on arXiv 15 May 2026.

Builds on

35 of 94 references.

References · 35

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23 more
  1. 2021Multi-view Contrastive Graph Clusteringcited by 76
  2. 2021Multi-level Feature Learning for Contrastive Multi-view Clusteringcited by 73
  3. 2019Deep Spectral Clustering using Dual Autoencoder Networkcited by 71
  4. 2021Reconsidering Representation Alignment for Multi-view Clusteringcited by 57
  5. 2021Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clusteringcited by 51
  6. 2021Modality-aware Mutual Learning for Multi-modal Medical Image Segmentationcited by 47
  7. 2021Self-Supervised Discriminative Feature Learning for Deep Multi-View Clusteringcited by 41
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  9. 2023Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignmentcited by 33
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  15. 2023DealMVC: Dual Contrastive Calibration for Multi-view Clusteringcited by 19
  16. 2023A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspectivecited by 14
  17. 2025Incomplete Multi-view Clustering via Diffusion Contrastive Generationcited by 13
  18. 2023On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clusteringcited by 10
  19. 2020Dual Adversarial Auto-Encoders for Clusteringcited by 7
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Clearer Sight, Fewer Lies: Oriented Pickup Preference Optimization for Multimodal Hallucination Mitigationcited by 1
  2. 2026SPORT: Structure-Aware Prototype Disentanglement for Incomplete Multi-View Clusteringcited by 0