mlsanityData to 7 Oct 2026

Improving Unsupervised Image Clustering With Robust Learning

arXiv 2012.11150 ↗First posted 21 Dec 2020cs.CV cs.AI cs.LGcited by 51

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Abstract (arXiv)

Unsupervised image clustering methods often introduce alternative objectives to indirectly train the model and are subject to faulty predictions and overconfident results. To overcome these challenges, the current research proposes an innovative model RUC that is inspired by robust learning. RUC's novelty is at utilizing pseudo-labels of existing image clustering models as a noisy dataset that may include misclassified samples. Its retraining process can revise misaligned knowledge and alleviate the overconfidence problem in predictions. The model's flexible structure makes it possible to be used as an add-on module to other clustering methods and helps them achieve better performance on multiple datasets. Extensive experiments show that the proposed model can adjust the model confidence with better calibration and gain additional robustness against adversarial noise.

Key facts. Cited by 51 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Dec 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 9; 2022: 14; 2023: 14; 2024: 6; 2025: 7; 2026: 1.

51 papers.

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  1. 2022Active Learning on a Budget: Opposite Strategies Suit High and Low Budgetscited by 131
  2. 2021SPICE: Semantic Pseudo-labeling for Image Clusteringcited by 104
  3. 2022A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directionscited by 73
  4. 2022Twin Contrastive Learning for Online Clusteringcited by 60
  5. 2022Neural Manifold Clustering and Embeddingcited by 44
  6. 2022FedX: Unsupervised Federated Learning with Cross Knowledge Distillationcited by 40
  7. 2021You Never Cluster Alonecited by 38
  8. 2023Unsupervised Universal Image Segmentationcited by 34
  9. 2021Unsupervised Selective Labeling for More Effective Semi-Supervised Learningcited by 27
  10. 2023Image Clustering Conditioned on Text Criteriacited by 26
  11. 2022Semantic-Enhanced Image Clusteringcited by 25
  12. 2021Clustering by Maximizing Mutual Information Across Viewscited by 23
38 more
  1. 2023Image Clustering via the Principle of Rate Reduction in the Age of Pretrained Modelscited by 22
  2. 2024Text Clustering as Classification with LLMscited by 16
  3. 2023Unsupervised Manifold Linearizing and Clusteringcited by 12
  4. 2023FedDefender: Client-Side Attack-Tolerant Federated Learningcited by 12
  5. 2022Self-supervised Context-aware Style Representation for Expressive Speech Synthesiscited by 10
  6. 2021ScatSimCLR: self-supervised contrastive learning with pretext task regularization for small-scale datasetscited by 9
  7. 2021Adaptive Attribute and Structure Subspace Clustering Networkcited by 9
  8. 2022Unsupervised Learning under Latent Label Shiftcited by 9
  9. 2025Exploring a Principled Framework for Deep Subspace Clusteringcited by 7
  10. 2021Elsa: Energy-based learning for semi-supervised anomaly detectioncited by 6
  11. 2023Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learningcited by 6
  12. 2025Generalizable Slum Detection from Satellite Imagery with Mixture-of-Expertscited by 6
  13. 2022Generalised Mutual Information for Discriminative Clusteringcited by 5
  14. 2022Collaborating Domain-shared and Target-specific Feature Clustering for Cross-domain 3D Action Recognitioncited by 4
  15. 2023Heterogeneous Tri-stream Clustering Networkcited by 3
  16. 2023Visual Self-paced Iterative Learning for Unsupervised Temporal Action Localizationcited by 3
  17. 2024Deep Clustering with Self-Supervision using Pairwise Similaritiescited by 3
  18. 2021Neural Mixture Models with Expectation-Maximization for End-to-end Deep Clusteringcited by 2
  19. 2021Joint Debiased Representation Learning and Imbalanced Data Clusteringcited by 2
  20. 2022On the Robustness of Deep Clustering Models: Adversarial Attacks and Defensescited by 2
  21. 2023High-dimensional Clustering onto Hamiltonian Cyclecited by 2
  22. 2023MV-MR: multi-views and multi-representations for self-supervised learning and knowledge distillationcited by 1
  23. 2023ContraCluster: Learning to Classify without Labels by Contrastive Self-Supervision and Prototype-Based Semi-Supervisioncited by 1
  24. 2023Generalised Mutual Information: a Framework for Discriminative Clusteringcited by 1
  25. 2024Enhancing Interpretability in Deep Reinforcement Learning through Semantic Clusteringcited by 1
  26. 2026ERA: Evidence-based Reliability Alignment for Honest Retrieval-Augmented Generationcited by 1
  27. 2022SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical Processescited by 0
  28. 2022Joint Debiased Representation and Image Clustering Learning with Self-Supervisioncited by 0
  29. 2022Improving Image Clustering through Sample Ranking and Its Application to remote--sensing imagescited by 0
  30. 2023Shuffle & Divide: Contrastive Learning for Long Textcited by 0
  31. 2023Towards Generalized Multi-stage Clustering: Multi-view Self-distillationcited by 0
  32. 2024GCC: Generative Calibration Clusteringcited by 0
  33. 2024Clustering-friendly Representation Learning for Enhancing Salient Featurescited by 0
  34. 2024An Approach Towards Learning K-means-friendly Deep Latent Representationcited by 0
  35. 2025ConstellationNet: Reinventing Spatial Clustering through GNNscited by 0
  36. 2025A Tutorial on Discriminative Clustering and Mutual Informationcited by 0
  37. 2025Enhancing Lung Disease Diagnosis via Semi-Supervised Machine Learningcited by 0
  38. 2025An Adaptor for Triggering Semi-Supervised Learning to Out-of-Box Serve Deep Image Clusteringcited by 0

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