Generative Diffusion Contrastive Network for Multi-View Clustering

Jian Zhu, Xin Zou, Xi Wang, Lei Liu, Chang Tang +1 more
9/11/2025
cs.CV

Official code: HackerHyper/GDCN (25 stars, last commit 2026-02-28).

Abstract

In recent years, Multi-View Clustering (MVC) has been significantly advanced under the influence of deep learning. By integrating heterogeneous data from multiple views, MVC enhances clustering analysis, making multi-view fusion critical to clustering performance. However, there is a problem of low-quality data in multi-view fusion. This problem primarily arises from two reasons: 1) Certain views are contaminated by noisy data. 2) Some views suffer from missing data. This paper proposes a novel Stochastic Generative Diffusion Fusion (SGDF) method to address this problem. SGDF leverages a multiple generative mechanism for the multi-view feature of each sample. It is robust to low-quality data. Building on SGDF, we further present the Generative Diffusion Contrastive Network (GDCN). Extensive experiments show that GDCN achieves the state-of-the-art results in deep MVC tasks. The source code is publicly available at https://github.com/HackerHyper/GDCN.

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Code Implementations(1)

HackerHyper/GDCNOfficial100%

Generative Diffusion Contrastive Network for Multi-View Clustering

251PythonSep 7, 20257 months ago

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Cite this paper

@article{zhu2025generative,
  title  = {Generative Diffusion Contrastive Network for Multi-View Clustering},
  author = {Jian Zhu and Xin Zou and Xi Wang and Lei Liu and Chang Tang and Li-Rong Dai},
  year   = {2025},
  eprint = {2509.09527},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.09527v2}
}

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