Self-Supervised Learning

Also known as SSL

Self-supervised learning creates training signals from the data itself, for example by matching augmented views, predicting masked content, or using temporal, geometric, and physical consistency. Contrastive learning, masked autoencoders, teacher–student distillation, and joint-embedding predictive architectures are common approaches across images, video, audio, language, medical data, and scientific measurements. These methods can reduce dependence on costly labels and support transfer, but useful representations depend on how well the pretext signal fits the data and downstream task. A central tension is balancing invariance and non-collapse with retaining fine-grained information, while establishing reliable transfer under domain shifts and limited compute remains an open challenge.

Latest papers

261 papers32 papers in the last four weeks, up 33% on the four weeks before. 0.2% of all new papers.

SPHERE-JEPA: Spherical Prediction with Homogeneous Embeddings
May 26, 2026L{é}o Nicollier, Max Dunitz, Marc Pic +3Self-Supervised Learning Joint-Embedding Predictive Architecture
CB,ATT · ATT · CB,IFUMI +1
Geometry-Aware Contrastive Learning for Few-Shot Automatic Modulation Recognition
May 26, 2026Guanqun Zhao, Yitong Liu, Jiaxuan Fang +2Contrastive Learning Self-Supervised Learning
Beijing University of Posts and Telecommunications, Beijing, China
Physics-Guided Self-Supervised Statistical Residual Learning for Sonar Despeckling with Improved Generalization
May 23, 2026Swapna Pillai, Siddharth Singh Savner, Sujit Kumar SahooSelf-Supervised Image Denoising Self-Supervised Learning
School of Electrical Sciences, Indian Institute of Technology Goa, Goa 403401, India · Inria, Sophia Antipolis, France
ImPartial: Multi-channel Whole-Cell Segmentation using Partial Annotations
May 22, 2026Gunjan Shrivastava, Saad NadeemImage Segmentation Weakly Supervised Image Segmentation
Memorial Sloan Kettering Cancer Center, New York NY 10065, USA
LaMo: Self-Supervised Latent Motion Priors for Physical Realism in Video Generation
May 22, 2026Bo Jiang, Depu Meng, Yihan Hu +3Physical Consistency in Video Generation Diffusion Model Guidance
Applied Intuition · University of California, Berkeley
Divide and Contrast: Learning Robust Temporal Features without Augmentation
May 20, 2026Abdul-Kazeem Shamba, Kerstin Bach, Gavin TaylorContrastive Learning Self-Supervised Learning
Department of Computer Science, Norwegian University of Science and Technology, Norway · Department of Computer Science, United States Naval Academy, USA.
Self-supervised local learning rules learn the hidden hierarchical structure of high-dimensional data
May 18, 2026Ariane Delrocq, Wu S. Zihan, Guillaume Bellec +1Self-Supervised Learning Hierarchical Representation Learning
School of Life Science and School of Computer and Communications Sciences, EPFL, Lausanne, Switzerland · Machine Learning Research Unit, TU Wien, Vienna, Austria
Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Task-Oriented Review with Practical Design Guidelines
May 18, 2026Chathura Wimalasiri, Kishor Nandakishor, Marimuthu PalaniswamiContrastive Learning Self-Supervised Learning
Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC 3052, Australia
UST-Hand: An Uncertainty-aware Spatiotemporal Point Cloud Interaction Network for 3D Self-supervised Hand Pose Estimation
May 18, 2026Tianhao Han, Haoyang Zhang, Liang Xie +5Uncertainty Quantification Self-Supervised Learning
School of Computer Science, Shanghai Jiao Tong University · Defense Innovation Institute, Academy of Military Sciences · Tianjin Artificial Intelligence Innovation Center +3
PEIRA: Learning Predictive Encoders through Inter-View Regressor Alignment
May 17, 2026Michael Arbel, Basile Terver, Jean PonceUnsupervised Learning Visual Representation Learning
Univ. Grenoble Alpes, Inria · CNRS, Grenoble INP, LJK · Ecole Normale Supérieure / PSL +2
The Geometry of Projection Heads: Conditioning, Invariance, and Collapse
May 16, 2026Faris ChaudhryContrastive Learning Neural Representation Geometry
Department of Computing, Imperial College London, United Kingdom.
SOLAR: Self-supervised Joint Learning for Symmetric Multimodal Retrieval
May 15, 2026Wenjie Yang, Hang Yu, Yuyu Guo +1Cross-Modal Learning Multimodal IR
Ant Group
Network-Aware Bilinear Tokenization for Brain Functional Connectivity Representation Learning
May 13, 2026Leo Milecki, Qingyu Hu, Bahram Jafrasteh +2Representation Learning Masked Autoencoders
Department of Radiology, Weill Cornell Medicine, New York, NY, USA. · School of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, NY, USA.
R2R2: Robust Representation for Intensive Experience Reuse via Redundancy Reduction in Self-Predictive Learning
May 13, 2026Sanghyeob Song, Donghyeok Lee, Jinsik Kim +1Reinforcement Learning Self-Supervised Learning
Interdisciplinary Program in Artificial Intelligence, Seoul National University · Samsung Electro-Mechanics, Republic of Korea · Department of Electrical and Computer Engineering, Seoul National University
EvoGround: Self-Evolving Video Agents for Video Temporal Grounding
May 13, 2026Minjoon Jung, Byoung-Tak Zhang, Lorenzo TorresaniTemporal Video Grounding Self-Supervised Learning
Seoul National University · Northeastern University
Modulation Consistency-based Contrastive Learning for Self-Supervised Automatic Modulation Classification
May 12, 2026Chenxu Wang, Shuang Wang, Lirong Han +4Contrastive Learning Self-Supervised Learning
School of Artificial Intelligence, Xidian University, Xi’an 710071, China · Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, China · School of Electronics And Information, Northwestern Polytechnical University, Xi’an 710072, China
Information theoretic underpinning of self-supervised learning by clustering
May 12, 2026Josef Kittler, Sara Atito, Muhammad AwaisSelf-Supervised Learning Unsupervised Clustering
Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey, Guildford, Surrey GU2 7XH, UK · Surrey Institute for People-Centred AI (PAI), University of Surrey, Guildford, Surrey GU2 7XH, UK
Martingale-Consistent Self-Supervised Learning
May 12, 2026Moritz Gögl, Hanwen Xing, Christopher YauSelf-Supervised Learning Self-Supervised Time Series Representation Learning
University of Oxford · Health Data Research UK
Deploying Self-Supervised Learning for Real Seismic Data Denoising
May 11, 2026Giovanny A. M. Arboleda, Claudio D. T. de Souza, Carlos E. M. dos Anjos +6Self-Supervised Image Denoising Self-Supervised Learning
COPPE Federal University of Rio de Janeiro Rio de Janeiro, Brazil · CENPES, Petrobras Rio de Janeiro, Brazil
FrequencyCT: Frequency Domain Self-supervised Low-dose CT Denoising
May 11, 2026Guoquan Wei, Liu Shi, Chong Chen +1Self-Supervised Image Denoising Self-Supervised Learning
School of Information Engineering, Nanchang University, Nanchang 330031, China · SKLMS, ICMSEC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
Team-Based Self-Play With Dual Adaptive Weighting for Fine-Tuning LLMs
May 11, 2026Wu Li, Yigeng Zhou, Zesheng Shi +3LLM Alignment Self-Play RL
Harbin Institute of Technology, Shenzhen, China · Beijing Academy of Artificial Intelligence, Beijing, China
Inpainting physics: self-supervised learning for context-driven fluid simulation
May 9, 2026Jonas Weidner, Yeray Martin-Ruisanchez, Daniel Rueckert +2Neural Surrogate Modeling Self-Supervised Learning
AI for Image-Guided Diagnosis and Therapy, Technical University of Munich · Munich Center for Machine Learning (MCML) · AI in Healthcare and Medicine, Technical University of Munich +1
Improved monocular depth prediction using distance transform over pre-semantic contours with self-supervised neural networks
May 8, 2026Marwane Hariat, Antoine Manzanera, David FilliatSelf-Supervised Learning Depth Estimation
U2IS, ENSTA, Institut Polytechnique de Paris, Palaiseau, France
Rebalancing gradient to improve self-supervised co-training of depth, odometry and optical flow predictions
May 8, 2026Marwane Hariat, Antoine Manzanera, David FilliatOptical Flow Self-Supervised Learning
1U2IS, ENSTA Paris, Institut Polytechnique de Paris, Palaiseau, France · 2INRIA FLOWERS
ProtoSSL: Self-Supervised Pretraining and Downstream Transfer for Projection-Based Prototype Models
May 7, 2026Steven Song, Sahil Sethi, Brett Beaulieu-Jones +1Self-Supervised Learning Self-Supervised Pre-Training
Department of Computer Science, University of Chicago, Chicago, IL 60637, USA · Center for Translational Data Science, University of Chicago, Chicago, IL 60637, USA · Medical Scientist Training Program, University of Chicago, Chicago, IL 60637, USA +3
Enabling Unsupervised Training of Deep EEG Denoisers With Intelligent Partitioning
May 7, 2026Qiyu Rao, Haozhe Tian, Homayoun Hamedmoghadam +1Self-Supervised Image Denoising Electroencephalography
Department of Electrical and Electronic Engineering, Imperial College London · Dyson School of Design Engineering, Imperial College London
TinySSL: Distilled Self-Supervised Pretraining for Sub-Megabyte MCU Models
May 7, 2026Bibin WilsonSelf-Supervised Learning Self-Supervised Pre-Training
Understanding Self-Supervised Learning via Latent Distribution Matching
May 5, 2026Fabian A Mikulasch, Friedemann ZenkeRepresentation Learning Self-Supervised Learning
Friedrich Miescher Institute for Biomedical Research, 4056 Basel, Switzerland · Faculty of Science, University of Basel, 4033 Basel, Switzerland
Enhancing Self-Supervised Talking Head Forgery Detection via a Training-Free Dual-System Framework
May 5, 2026Ke Liu, Jiwei Wei, Shuchang Zhou +5Self-Supervised Learning Dual-Process Reasoning
University of Electronic Science and Technology of China Chengdu, China · Hainan University Haikou, China
TumorXAI: Self-Supervised Deep Learning Framework for Explainable Brain MRI Tumor Classification
May 3, 2026Abrar Hossain Zahin, Amit Kumar Saha, Tanvir Mridha +5Self-Supervised Learning Medical Image Classification
Department of Computer Science and Engineering, East West University, Dhaka, 1212, Bangladesh.

May 26, 2026J/K move · Enter open · S save

SPHERE-JEPA: Spherical Prediction with Homogeneous Embeddings

L{é}o Nicollier, Max Dunitz, Marc Pic, Pablo Mus{é}+2

CB,ATT · ATT · CB,IFUMI+1

Self-Supervised Learning Joint-Embedding Predictive Architecture Hyperspherical Representation Learning Geometric Representation Learning Manifold Learning Latent Space Geometry

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A fundamental open question in self-supervised learning (SSL) is the explicit characterization of the optimal geometry of the learned representations. Recently, LeJEPA identified isotropic Gaussian embeddings as optimal for minimizing downstream prediction risk in Euclidean spaces. However, the corresponding problem for distributions supported on lower-dimensional manifolds, such as the hypersphere, remains unexplored. In this work, we demonstrate that extending this minimax analysis to smooth distributions on Riemannian manifolds fundamentally changes the optimal solution. We show that, under a worst-case formulation, both k-nearest neighbors and kernel ridge regression induce hyperspherical uniformity. More precisely, we show that uniform distributions on manifolds are optimal for k-nearest neighbors, and that the uniform distribution on the sphere is optimal for kernel ridge regression with both the exponential dot-product kernel and the linear kernel. This theoretical insight reveals a fundamental limitation of Gaussian embeddings: their non-uniform density induces anisotropic k-NN neighborhoods, severely biasing the estimator. To correct this, we introduce SPHERE-JEPA, a theoretically grounded SSL framework. We adapt LeJEPA's Cram{é}r-Wold projection mechanism to enforce hyperspherical uniformity rather than a Gaussian prior. Empirically, SPHERE-JEPA yields significant improvements, boosting texture retrieval mAP by over 6%, while consistently matching or outperforming LeJEPA on standard benchmarks-including a +1.8% linear probing gain on ImageNet-1K (ViT-B/14).