mlsanityData to 14 Oct 2026

Representation Learning via Consistent Assignment of Views to Clusters

arXiv 2112.15421 ↗First posted 31 Dec 2021cs.LG cs.CVcited by 8

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Abstract (arXiv)

We introduce Consistent Assignment for Representation Learning (CARL), an unsupervised learning method to learn visual representations by combining ideas from self-supervised contrastive learning and deep clustering. By viewing contrastive learning from a clustering perspective, CARL learns unsupervised representations by learning a set of general prototypes that serve as energy anchors to enforce different views of a given image to be assigned to the same prototype. Unlike contemporary work on contrastive learning with deep clustering, CARL proposes to learn the set of general prototypes in an online fashion, using gradient descent without the necessity of using non-differentiable algorithms or K-Means to solve the cluster assignment problem. CARL surpasses its competitors in many representations learning benchmarks, including linear evaluation, semi-supervised learning, and transfer learning.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 30 of its 39 references that are in the map. First posted on arXiv 31 Dec 2021.

Builds on

30 of 39 references.

References · 30

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18 more
  1. 2017Accurate, Large Minibatch SGD: Training ImageNet in 1 Hourcited by 1,492
  2. 2016Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzlescited by 1,382
  3. 2020Big Self-Supervised Models are Strong Semi-Supervised Learnerscited by 1,380
  4. 2019Contrastive Multiview Codingcited by 1,301
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  12. 2020Prototypical Contrastive Learning of Unsupervised Representationscited by 640
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Cited by year

Citing papers per year: 2023: 2; 2024: 3; 2025: 3.

8 papers.

  1. 2024Learning from Memory: Non-Parametric Memory Augmented Self-Supervised Learning of Visual Featurescited by 6
  2. 2023Self-supervised Learning of Contextualized Local Visual Embeddingscited by 2
  3. 2023Representation Learning via Consistent Assignment of Views over Random Partitionscited by 2
  4. 2024ComFe: An Interpretable Head for Vision Transformerscited by 2
  5. 2025Self-Organizing Visual Prototypes for Non-Parametric Representation Learningcited by 2
  6. 2025Comprehensive Evaluation of Prototype Neural Networkscited by 1
  7. 2024What Do You See in Common? Learning Hierarchical Prototypes over Tree-of-Life to Discover Evolutionary Traitscited by 0
  8. 2025Why Prototypes Collapse: Diagnosing and Preventing Partial Collapse in Prototypical Self-Supervised Learningcited by 0