mlsanityData to 14 Oct 2026

Clustering via Self-Supervised Diffusion

arXiv 2507.04283 ↗First posted 6 Jul 2025cs.AI cs.CVcited by 3

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

Diffusion models, widely recognized for their success in generative tasks, have not yet been applied to clustering. We introduce Clustering via Diffusion (CLUDI), a self-supervised framework that combines the generative power of diffusion models with pre-trained Vision Transformer features to achieve robust and accurate clustering. CLUDI is trained via a teacher-student paradigm: the teacher uses stochastic diffusion-based sampling to produce diverse cluster assignments, which the student refines into stable predictions. This stochasticity acts as a novel data augmentation strategy, enabling CLUDI to uncover intricate structures in high-dimensional data. Extensive evaluations on challenging datasets demonstrate that CLUDI achieves state-of-the-art performance in unsupervised classification, setting new benchmarks in clustering robustness and adaptability to complex data distributions. Our code is available at https://github.com/BGU-CS-VIL/CLUDI.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 48 of its 67 references that are in the map. First posted on arXiv 6 Jul 2025.

Builds on

48 of 67 references.

Closest ancestors · 11

Same direction, newest first

  1. 2024Step-by-Step Diffusion: An Elementary Tutorial13 Jun
  2. 2024Tutorial on Diffusion Models for Imaging and Vision26 Mar
  3. 2024Denoising Diffusion Probabilistic Models in Six Simple Steps6 Feb
  4. 2023Efficient Diffusion Training via Min-SNR Weighting Strategy16 Mar
  5. 2022Understanding Diffusion Models: A Unified Perspective25 Aug
  6. 2021High-Resolution Image Synthesis with Latent Diffusion Models20 Dec
  7. 2021Diffusion Models Beat GANs on Image Synthesis11 May
  8. 2021Improved Denoising Diffusion Probabilistic Models18 Feb
  9. 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov
  10. 2020Denoising Diffusion Implicit Models6 Oct
  11. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 37

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  3. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  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. 2018Deep Clustering for Unsupervised Learning of Visual Featurescited by 1,028
  8. 2022Diffusion-LM Improves Controllable Text Generationcited by 1,027
  9. 2020Meta-Learning in Neural Networks: A Surveycited by 950
  10. 2015Unsupervised Deep Embedding for Clustering Analysiscited by 767
  11. 2020A Survey on Contrastive Self-supervised Learningcited by 554
  12. 2018Invariant Information Clustering for Unsupervised Image Classification and Segmentationcited by 455
25 more
  1. 2022DiffuSeq: Sequence to Sequence Text Generation with Diffusion Modelscited by 450
  2. 2020SCAN: Learning to Classify Images without Labelscited by 352
  3. 2021Understanding self-supervised Learning Dynamics without Contrastive Pairscited by 256
  4. 2016Variational Deep Embedding: An Unsupervised and Generative Approach to Clusteringcited by 220
  5. 2022Continuous diffusion for categorical datacited by 214
  6. 2020How Well Do Self-Supervised Models Transfer?cited by 213
  7. 2023A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trendscited by 207
  8. 2022Deep Clustering: A Comprehensive Surveycited by 78
  9. 2022A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directionscited by 73
  10. 2023To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Reviewcited by 72
  11. 2021Learning Representation for Clustering via Prototype Scattering and Positive Samplingcited by 58
  12. 2022DeepDPM: Deep Clustering With an Unknown Number of Clusterscited by 52
  13. 2021Exploring the Equivalence of Siamese Self-Supervised Learning via A Unified Gradient Frameworkcited by 49
  14. 2022Empowering Diffusion Models on the Embedding Space for Text Generationcited by 40
  15. 2023Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Trainingcited by 39
  16. 2023Exploring the Limits of Deep Image Clustering using Pretrained Modelscited by 33
  17. 2021Self-Supervised Classification Networkcited by 28
  18. 2021Towards Demystifying Representation Learning with Non-contrastive Self-supervisioncited by 23
  19. 2021Deep Clustering based Fair Outlier Detectioncited by 19
  20. 2022Implicit variance regularization in non-contrastive SSLcited by 16
  21. 2023The Edge of Orthogonality: A Simple View of What Makes BYOL Tickcited by 15
  22. 2022Deep Clustering with Features from Self-Supervised Pretrainingcited by 14
  23. 2018Clustering - What Both Theoreticians and Practitioners are Doing Wrongcited by 8
  24. 2020Amortized Probabilistic Detection of Communities in Graphscited by 4
  25. 2025Consistent Amortized Clustering via Generative Flow Networkscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2026CRoCoDiL: Continuous and Robust Conditioned Diffusion for Languagecited by 1
  2. 2025DiEC: Diffusion Embedded Clusteringcited by 0
  3. 2026Converge to Surprise: Evolutionary Self-supervised Image Clusteringcited by 0

Similar, not cited

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  1. 2026Diffusion Models for High-Dimensional Clustered Data: Intrinsic-Dimension Adaptivity via Bayesian Classification
  2. 2024TreeDiffusion: Hierarchical Generative Clustering for Conditional Diffusion
  3. 2024Breaking the Curse of Dimensionality: Diffusion Models Efficiently Learn Low-Dimensional Distributions
  4. 2024DiFiC: Your Diffusion Model Holds the Secret to Fine-Grained Clustering
  5. 2025Compute Only 16 Tokens in One Timestep: Accelerating Diffusion Transformers with Cluster-Driven Feature Caching
  6. 2022Diffusion Models: A Comprehensive Survey of Methods and Applications
  7. 2024Data Pruning in Generative Diffusion Models
  8. 2024Diffusion Models and Representation Learning: A Survey