mlsanityData to 14 Oct 2026

Dual Consistent Constraint via Disentangled Consistency and Complementarity for Multi-view Clustering

arXiv 2504.04676 ↗First posted 7 Apr 2025cs.CV cs.AIcited by 0

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Abstract (arXiv)

Multi-view clustering can explore common semantics from multiple views and has received increasing attention in recent years. However, current methods focus on learning consistency in representation, neglecting the contribution of each view's complementarity aspect in representation learning. This limit poses a significant challenge in multi-view representation learning. This paper proposes a novel multi-view clustering framework that introduces a disentangled variational autoencoder that separates multi-view into shared and private information, i.e., consistency and complementarity information. We first learn informative and consistent representations by maximizing mutual information across different views through contrastive learning. This process will ignore complementary information. Then, we employ consistency inference constraints to explicitly utilize complementary information when attempting to seek the consistency of shared information across all views. Specifically, we perform a within-reconstruction using the private and shared information of each view and a cross-reconstruction using the shared information of all views. The dual consistency constraints are not only effective in improving the representation quality of data but also easy to extend to other scenarios, especially in complex multi-view scenes. This could be the first attempt to employ dual consistent constraint in a unified MVC theoretical framework. During the training procedure, the consistency and complementarity features are jointly optimized. Extensive experiments show that our method outperforms baseline methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 35 of its 97 references that are in the map. First posted on arXiv 7 Apr 2025.

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35 of 97 references.

References · 35

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  2. 2018Representation Learning with Contrastive Predictive Codingcited by 7,777
  3. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  4. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  5. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,750
  6. 2020Exploring Simple Siamese Representation Learningcited by 2,866
  7. 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,857
  8. 2016InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Netscited by 860
  9. 2020Contrastive Multi-View Representation Learning on Graphscited by 652
  10. 2020Contrastive Clusteringcited by 265
  11. 2016Variational Deep Embedding: An Unsupervised and Generative Approach to Clusteringcited by 220
  12. 2020Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-IDcited by 212
23 more
  1. 2018Multimodal Generative Models for Scalable Weakly-Supervised Learningcited by 200
  2. 2020Learning Robust Representations via Multi-View Information Bottleneckcited by 191
  3. 2019Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Modelscited by 178
  4. 2021SPICE: Semantic Pseudo-labeling for Image Clusteringcited by 104
  5. 2016Joint Multimodal Learning with Deep Generative Modelscited by 87
  6. 2018Learning Disentangled Joint Continuous and Discrete Representationscited by 83
  7. 2021Multi-level Feature Learning for Contrastive Multi-view Clusteringcited by 73
  8. 2021Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clusteringcited by 51
  9. 2021Self-Supervised Discriminative Feature Learning for Deep Multi-View Clusteringcited by 41
  10. 2018Essential Tensor Learning for Multi-view Spectral Clusteringcited by 31
  11. 2020Deep Embedded Multi-view Clustering with Collaborative Trainingcited by 31
  12. 2020Generative Partial Multi-View Clusteringcited by 25
  13. 2022Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondencescited by 20
  14. 2023DealMVC: Dual Contrastive Calibration for Multi-view Clusteringcited by 19
  15. 2019Shared Generative Latent Representation Learning for Multi-view Clusteringcited by 17
  16. 2018Incomplete Multi-view Clustering via Graph Regularized Matrix Factorizationcited by 15
  17. 2020Unsupervised Domain Adaptation for Person Re-Identification through Source-Guided Pseudo-Labelingcited by 12
  18. 2023Semantic Invariant Multi-view Clustering with Fully Incomplete Informationcited by 12
  19. 2019Multi-View Multiple Clusterings using Deep Matrix Factorizationcited by 9
  20. 2024Learning the Unlearned: Mitigating Feature Suppression in Contrastive Learningcited by 9
  21. 2022Self-supervised Image Clustering from Multiple Incomplete Views via Constrastive Complementary Generationcited by 8
  22. 2020Private-Shared Disentangled Multimodal VAE for Learning of Hybrid Latent Representationscited by 7
  23. 2024Balancing Complementarity and Consistency via Delayed Activation in Incomplete Multi-view Clusteringcited by 1

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