mlsanityData to 7 Oct 2026

Solo-learn: A Library of Self-supervised Methods for Visual Representation Learning

arXiv 2108.01775 ↗First posted 3 Aug 2021cs.CVcited by 116

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Abstract (arXiv)

This paper presents solo-learn, a library of self-supervised methods for visual representation learning. Implemented in Python, using Pytorch and Pytorch lightning, the library fits both research and industry needs by featuring distributed training pipelines with mixed-precision, faster data loading via Nvidia DALI, online linear evaluation for better prototyping, and many additional training tricks. Our goal is to provide an easy-to-use library comprising a large amount of Self-supervised Learning (SSL) methods, that can be easily extended and fine-tuned by the community. solo-learn opens up avenues for exploiting large-budget SSL solutions on inexpensive smaller infrastructures and seeks to democratize SSL by making it accessible to all. The source code is available at https://github.com/vturrisi/solo-learn.

Key facts. Cited by 116 papers in the map (data through 7 Oct 2026). Builds on 16 of its 20 references that are in the map. First posted on arXiv 3 Aug 2021.

Builds on

16 of 20 references.

References · 16

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  4. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  5. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
  6. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,712
  7. 2020Exploring Simple Siamese Representation Learningcited by 2,853
  8. 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,845
  9. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
  10. 2020Supervised Contrastive Learningcited by 2,803
  11. 2020Improved Baselines with Momentum Contrastive Learningcited by 2,282
  12. 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,818
4 more
  1. 2021VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learningcited by 1,013
  2. 2021With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representationscited by 324
  3. 2020Whitening for Self-Supervised Representation Learningcited by 278
  4. 2021ReSSL: Relational Self-Supervised Learning with Weak Augmentationcited by 83

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 28; 2023: 29; 2024: 27; 2025: 22; 2026: 9.

116 papers.

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  1. 2021Self-Supervised Models are Continual Learnerscited by 149
  2. 2022Adversarial Masking for Self-Supervised Learningcited by 78
  3. 2022How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learningcited by 65
  4. 2022Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCocited by 52
  5. 2022Spectral Feature Augmentation for Graph Contrastive Learning and Beyondcited by 49
  6. 2022Neural Manifold Clustering and Embeddingcited by 44
  7. 2023Is ImageNet worth 1 video? Learning strong image encoders from 1 long unlabelled videocited by 40
  8. 2023Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Trainingcited by 39
  9. 2022Self-Supervised Learning with an Information Maximization Criterioncited by 38
  10. 2025Joint Embedding vs Reconstruction: Provable Benefits of Latent Space Prediction for Self Supervised Learningcited by 33
  11. 2023Towards a Unified Theoretical Understanding of Non-contrastive Learning via Rank Differential Mechanismcited by 32
  12. 2023Identifying Interpretable Subspaces in Image Representationscited by 32
38 more
  1. 2023Downstream-agnostic Adversarial Examplescited by 32
  2. 2023Learning Efficient Coding of Natural Images with Maximum Manifold Capacity Representationscited by 31
  3. 2023Group Robust Classification Without Any Group Informationcited by 25
  4. 2024Do Generated Data Always Help Contrastive Learning?cited by 25
  5. 2022How Robust is Unsupervised Representation Learning to Distribution Shift?cited by 24
  6. 2022Self-Supervised Pretraining for 2D Medical Image Segmentationcited by 24
  7. 2022Simplicial Embeddings in Self-Supervised Learning and Downstream Classificationcited by 23
  8. 2022Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustnesscited by 20
  9. 2023Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learningcited by 20
  10. 2023Semi-supervised learning made simple with self-supervised clusteringcited by 20
  11. 2023EMP-SSL: Towards Self-Supervised Learning in One Training Epochcited by 18
  12. 2023Class Incremental Learning with Self-Supervised Pre-Training and Prototype Learningcited by 18
  13. 2022Is Self-Supervised Learning More Robust Than Supervised Learning?cited by 17
  14. 2022Unsupervised visualization of image datasets using contrastive learningcited by 16
  15. 2022Implicit variance regularization in non-contrastive SSLcited by 15
  16. 2023All4One: Symbiotic Neighbour Contrastive Learning via Self-Attention and Redundancy Reductioncited by 11
  17. 2022On the Pros and Cons of Momentum Encoder in Self-Supervised Visual Representation Learningcited by 10
  18. 2023PILLAR: How to make semi-private learning more effectivecited by 10
  19. 2024Making Better Use of Unlabelled Data in Bayesian Active Learningcited by 10
  20. 2022Bag of Image Patch Embedding Behind the Success of Self-Supervised Learningcited by 9
  21. 2023Modulate Your Spectrum in Self-Supervised Learningcited by 9
  22. 2024Preventing Dimensional Collapse in Self-Supervised Learning via Orthogonality Regularizationcited by 9
  23. 2024Learning predictable and robust neural representations by straightening image sequencescited by 9
  24. 2025stable-pretraining-v1: Foundation Model Research Made Simplecited by 9
  25. 2023Learning Symbolic Representations Through Joint GEnerative and DIscriminative Trainingcited by 8
  26. 2023Systematic comparison of semi-supervised and self-supervised learning for medical image classificationcited by 8
  27. 2024Occam's Razor for Self Supervised Learning: What is Sufficient to Learn Good Representations?cited by 8
  28. 2025Clustering Properties of Self-Supervised Learningcited by 8
  29. 2026Rectified LpJEPA: Joint-Embedding Predictive Architectures with Sparse and Maximum-Entropy Representationscited by 8
  30. 2022Unsupervised Visual Representation Learning via Mutual Information Regularized Assignmentcited by 7
  31. 2022Self-Supervised Visual Representation Learning via Residual Momentumcited by 7
  32. 2024On Pretraining Data Diversity for Self-Supervised Learningcited by 7
  33. 2024Collapse-Proof Non-Contrastive Self-Supervised Learningcited by 7
  34. 2024HEX: Hierarchical Emergence Exploitation in Self-Supervised Algorithmscited by 7
  35. 2022Learning Task-Independent Game State Representations from Unlabeled Imagescited by 6
  36. 2022Towards Efficient and Effective Self-Supervised Learning of Visual Representationscited by 6
  37. 2022GEDI: GEnerative and DIscriminative Training for Self-Supervised Learningcited by 6
  38. 2023To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learningcited by 6

50 most cited of 116 All 116 (JSON)