mlsanityData to 14 Oct 2026

Cluster Contrast for Unsupervised Visual Representation Learning

arXiv 2507.12359 ↗First posted 16 Jul 2025cs.CV cs.AIcited by 2

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Abstract (arXiv)

We introduce Cluster Contrast (CueCo), a novel approach to unsupervised visual representation learning that effectively combines the strengths of contrastive learning and clustering methods. Inspired by recent advancements, CueCo is designed to simultaneously scatter and align feature representations within the feature space. This method utilizes two neural networks, a query and a key, where the key network is updated through a slow-moving average of the query outputs. CueCo employs a contrastive loss to push dissimilar features apart, enhancing inter-class separation, and a clustering objective to pull together features of the same cluster, promoting intra-class compactness. Our method achieves 91.40% top-1 classification accuracy on CIFAR-10, 68.56% on CIFAR-100, and 78.65% on ImageNet-100 using linear evaluation with a ResNet-18 backbone. By integrating contrastive learning with clustering, CueCo sets a new direction for advancing unsupervised visual representation learning.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 26 of its 31 references that are in the map. First posted on arXiv 16 Jul 2025.

Builds on

26 of 31 references.

References · 26

  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
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  10. 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,838
  11. 2021An Empirical Study of Training Self-Supervised Vision Transformerscited by 1,616
  12. 2021VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learningcited by 1,057
14 more
  1. 2016An overview of gradient descent optimization algorithmscited by 1,033
  2. 2018Deep Clustering for Unsupervised Learning of Visual Featurescited by 1,028
  3. 2019Self-Supervised Learning of Pretext-Invariant Representationscited by 808
  4. 2015Unsupervised Deep Embedding for Clustering Analysiscited by 767
  5. 2020Prototypical Contrastive Learning of Unsupervised Representationscited by 640
  6. 2020Contrastive Clusteringcited by 265
  7. 2020Representation Learning via Invariant Causal Mechanismscited by 199
  8. 2019Video Representation Learning by Dense Predictive Codingcited by 175
  9. 2021Solo-learn: A Library of Self-supervised Methods for Visual Representation Learningcited by 116
  10. 2020Online Deep Clustering for Unsupervised Representation Learningcited by 100
  11. 2021ReSSL: Relational Self-Supervised Learning with Weak Augmentationcited by 84
  12. 2021Self-Supervised Classification Networkcited by 28
  13. 2021Unsupervised Visual Representation Learning by Online Constrained K-Meanscited by 27
  14. 2022Unsupervised Visual Representation Learning by Synchronous Momentum Groupingcited by 22

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Open-World Semantic Segmentation with Sensitivity Modelingcited by 0
  2. 2026Three Necessary Principles for Self-Supervised Visual Representation Learningcited by 0