# The Hidden Uniform Cluster Prior in Self-Supervised Learning

arXiv: https://arxiv.org/abs/2210.07277 · HTML: https://mlsanity.com/paper/2210.07277/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- Builds on 53 of its 84 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 30 of 53)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning](https://mlsanity.com/paper/1612.06890/) (2016)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning](https://mlsanity.com/paper/2105.04906/) (2021)
- [iBOT: Image BERT Pre-Training with Online Tokenizer](https://mlsanity.com/paper/2111.07832/) (2021)
- [Self-Supervised Learning of Pretext-Invariant Representations](https://mlsanity.com/paper/1912.01991/) (2019)
- [Learning Representations by Maximizing Mutual Information Across Views](https://mlsanity.com/paper/1906.00910/) (2019)
- [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://mlsanity.com/paper/2202.03555/) (2022)
- [Exploring the Limits of Weakly Supervised Pretraining](https://mlsanity.com/paper/1805.00932/) (2018)

## Built on by (most cited first, 30 of 58)

- [Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2301.08243/) (2023)
- [V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning](https://mlsanity.com/paper/2506.09985/) (2025)
- [LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics](https://mlsanity.com/paper/2511.08544/) (2025)
- [A Cookbook of Self-Supervised Learning](https://mlsanity.com/paper/2304.12210/) (2023)
- [CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow](https://mlsanity.com/paper/2211.10408/) (2022)
- [Virchow: A Million-Slide Digital Pathology Foundation Model](https://mlsanity.com/paper/2309.07778/) (2023)
- [InfoNCE: Identifying the Gap Between Theory and Practice](https://mlsanity.com/paper/2407.00143/) (2024)
- [Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training](https://mlsanity.com/paper/2305.13689/) (2023)
- [On the Stepwise Nature of Self-Supervised Learning](https://mlsanity.com/paper/2303.15438/) (2023)
- [Learning Visual Representations via Language-Guided Sampling](https://mlsanity.com/paper/2302.12248/) (2023)
- [Group Robust Classification Without Any Group Information](https://mlsanity.com/paper/2310.18555/) (2023)
- [UniCorn: A Unified Contrastive Learning Approach for Multi-view Molecular Representation Learning](https://mlsanity.com/paper/2405.10343/) (2024)
- [Objectives Matter: Understanding the Impact of Self-Supervised Objectives on Vision Transformer Representations](https://mlsanity.com/paper/2304.13089/) (2023)
- [Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget](https://mlsanity.com/paper/2304.10520/) (2023)
- [3D-JEPA: A Joint Embedding Predictive Architecture for 3D Self-Supervised Representation Learning](https://mlsanity.com/paper/2409.15803/) (2024)
- [Self-Supervised Pre-Training with Joint-Embedding Predictive Architecture Boosts ECG Classification Performance](https://mlsanity.com/paper/2410.13867/) (2024)
- [What Makes CLIP More Robust to Long-Tailed Pre-Training Data? A Controlled Study for Transferable Insights](https://mlsanity.com/paper/2405.21070/) (2024)
- [Stochastic positional embeddings improve masked image modeling](https://mlsanity.com/paper/2308.00566/) (2023)
- [Combating Representation Learning Disparity with Geometric Harmonization](https://mlsanity.com/paper/2310.17622/) (2023)
- [Unsupervised Embedding Quality Evaluation](https://mlsanity.com/paper/2305.16562/) (2023)
- [Learning the Unlearned: Mitigating Feature Suppression in Contrastive Learning](https://mlsanity.com/paper/2402.11816/) (2024)
- [Clustering Properties of Self-Supervised Learning](https://mlsanity.com/paper/2501.18452/) (2025)
- [MambaMIM: Pre-training Mamba with State Space Token Interpolation and its Application to Medical Image Segmentation](https://mlsanity.com/paper/2408.08070/) (2024)
- [Collapse-Proof Non-Contrastive Self-Supervised Learning](https://mlsanity.com/paper/2410.04959/) (2024)
- [Improve Representation for Imbalanced Regression through Geometric Constraints](https://mlsanity.com/paper/2503.00876/) (2025)
- [Cross-view and Cross-pose Completion for 3D Human Understanding](https://mlsanity.com/paper/2311.09104/) (2023)
- [Beyond [cls]: Exploring the true potential of Masked Image Modeling representations](https://mlsanity.com/paper/2412.03215/) (2024)
- [On Partial Prototype Collapse in the DINO Family of Self-Supervised Methods](https://mlsanity.com/paper/2410.14060/) (2024)
- [A Tale of Two Classes: Adapting Supervised Contrastive Learning to Binary Imbalanced Datasets](https://mlsanity.com/paper/2503.17024/) (2025)
- [A surprisingly simple technique to control the pretraining bias for better transfer: Expand or Narrow your representation](https://mlsanity.com/paper/2304.05369/) (2023)

## Abstract (arXiv)

A successful paradigm in representation learning is to perform self-supervised pretraining using tasks based on mini-batch statistics (e.g., SimCLR, VICReg, SwAV, MSN). We show that in the formulation of all these methods is an overlooked prior to learn features that enable uniform clustering of the data. While this prior has led to remarkably semantic representations when pretraining on class-balanced data, such as ImageNet, we demonstrate that it can hamper performance when pretraining on class-imbalanced data. By moving away from conventional uniformity priors and instead preferring power-law distributed feature clusters, we show that one can improve the quality of the learned representations on real-world class-imbalanced datasets. To demonstrate this, we develop an extension of the Masked Siamese Networks (MSN) method to support the use of arbitrary features priors.