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.

MU-SHOT-Fi: Self-Supervised Multi-User Wi-Fi Sensing with Source-free Unsupervised Domain Adaptation
May 2, 2026Ahmed Y. Radwan, Hina TabassumSource-Free Domain Adaptation Human Activity Recognition
department of Electrical Engineering and Computer Science, York University, Toronto, ON M3J 1P3, Canada
MAEPose: Self-Supervised Spatiotemporal Learning for Human Pose Estimation on mmWave Video
Apr 30, 2026Xijia Wei, Yuan Fang, Kevin Chetty +2Masked Autoencoders Self-Supervised Learning
University College London London, UK
Action Motifs: Self-Supervised Hierarchical Representation of Human Body Movements
Apr 30, 2026Genki Kinoshita, Shu Nakamura, Ryo Kawahara +3Latent Action Learning Representation Learning
1Kyoto University · 2Kyoto Institute of Technology · 3RIKEN
Why Self-Supervised Encoders Want to Be Normal
Apr 30, 2026Yuval DombRepresentation Collapse Self-Supervised Learning
Intervention-Based Self-Supervised Learning: A Causal Probe Paradigm for Remote Photoplethysmography
Apr 26, 2026Zhiyi Niu, Xiaoguang Tu, Bo Zhao +3Remote Photoplethysmography Causal Representation Learning
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, PR China. · College of Aviation Electronic and Electrical Engineering, Civil Aviation Flight University of China, Guanghan 618307, PR China. · School of Computing and Information Technology, Great Bay University, Dongguan 523000, PR China.
H-SemiS: Hierarchical Fusion of Semi and Self-Supervised Learning for Knee Osteoarthritis Severity Grading
Apr 25, 2026Chandravardhan Singh Raghaw, Anushka Parwal, Shahid Shafi Dar +2Class-Imbalanced Learning Self-Supervised Learning
Department of Computer Science and Engineering, Indian Institute of Technology Indore, Indore 453552, India · Department of Computer Science and Engineering, National Institute of Technology Tiruchirappalli, Tiruchirappalli 620015, India
ArmSSL: Adversarial Robust Black-Box Watermarking for Self-Supervised Learning Pre-trained Encoders
Apr 24, 2026Yongqi Jiang, Yansong Gao, Boyu Kuang +3Self-Supervised Learning Robust Watermarking
Nanjing University of Science and Technology, China · The University of Western Australia, Australia · Zhejiang University, China +1
Trust-SSL: Additive-Residual Selective Invariance for Robust Aerial Self-Supervised Learning
Apr 23, 2026Wadii Boulila, Adel Ammar, Bilel Benjdira +1Contrastive Learning Image Corruption Robustness
Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh 11586, Saudi Arabia
Randomly Initialized Networks Can Learn from Peer-to-Peer Consensus
Apr 20, 2026Esteban Rodríguez-Betancourt, Edgar Casasola-MurilloRepresentation Learning Self-Supervised Learning
Posgrado en Computación e Informática Universidad de Costa Rica · Escuela de Ciencias de la Computación Universidad de Costa Rica
Beyond Binary Contrast: Modeling Continuous Skeleton Action Spaces with Transitional Anchors
Apr 20, 2026Yingjie Feng, Yi Wang, Jiaze Wang +2Skeleton-Based Action Recognition Contrastive Learning
Harbin Institute of Technology, Shenzhen · Central South University · FitX Technology (Hong Kong) Limited
Self-Supervised Super-Resolution for Sentinel-5P Hyperspectral Images
Apr 19, 2026Hyam Omar Ali, Antoine Crosnier, Romain Abraham +3Self-Supervised Learning Remote Sensing Image Super-Resolution
Université d’Orléans, Université de Tours, CNRS, IDP, UMR 7013, Orléans, France · Faculty of Mathematical Sciences, University of Khartoum, Sudan · ENS Lyon, France +2
Shape: A Self-Supervised 3D Geometry Foundation Model for Industrial CAD Analysis
Apr 19, 2026Bayangmbe Mounmo, Sam Chien, Mile Mitrovic3D Foundation Models Self-Supervised Learning
1SIMD AI · 2SB AI Lab
SSMamba: A Self-Supervised Hybrid State Space Model for Pathological Image Classification
Apr 17, 2026Enhui Chai, Sicheng Chen, Tianyi Zhang +2Self-Supervised Learning Pathology Foundation Models
School of Computer Science, Northwest University · PuzzleLogic Pte Ltd, Singapore 229594, Singapore · Department of Electrical & Computer Engineering, National University of Singapore +1
SPLIT: Self-supervised Partitioning for Learned Inversion in Nonlinear Tomography
Apr 17, 2026Markus Haltmeier, Lukas Neumann, Nadja Gruber +1Self-Supervised Learning Sparse-View CT Reconstruction
Physics-Informed Neural Networks with Learnable Loss Balancing and Transfer Learning
Apr 17, 2026Reza PirayeshshirazinezhadSelf-Supervised Learning Adaptive Loss Weighting
Texas A&M University College Station, TX, USA
Predict and Reconstruct: Joint Objectives for Self-Supervised Language Representation Learning
Apr 16, 2026Aimen BoukhariJoint-Embedding Predictive Architecture Masked Language Modeling
´Ecole Nationale Sup´erieure d’Informatique (ESI), Algiers , Algeria
Free Geometry: Refining 3D Reconstruction from Longer Versions of Itself
Apr 15, 2026Yuhang Dai, Xingyi YangFeed-Forward 3D Reconstruction Multi-View 3D Reconstruction
The Hong Kong Polytechnic University, Hong Kong SAR
From Alignment to Prediction: A Study of Self-Supervised Learning and Predictive Representation Learning
Apr 15, 2026Mintu Dutta, Ritesh Vyas, Mohendra RoySelf-Supervised Learning Predictive Representation Learning
Department of Information and Communication Technology, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat 382007, India
VibeFlow: Versatile Video Chroma-Lux Editing through Self-Supervised Learning
Apr 15, 2026Yifan Li, Pei Cheng, Bin Fu +2Video Relighting Video Editing
Wangxuan Institute of Computer Technology, Peking University · Tencent
Preventing Latent Rehearsal Decay in Online Continual SSL with SOLAR
Apr 12, 2026Giacomo Cignoni, Simone Magistri, Andrew D. Bagdanov +1Online Continual Learning Self-Supervised Learning
Department of Computer Science, University of Pisa, Pisa, Italy · Media Integration and Communication Center (MICC), University of Florence, Italy
A Minimal Model of Representation Collapse: Frustration, Stop-Gradient, and Dynamics
Apr 11, 2026Louie Hong Yao, Yuhao Li, Shengchao LiuRepresentation Collapse Self-Supervised Learning
Center for Soft Matter and Biological Physics, Virginia Tech, Blacksburg, VA 24061, USA · Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China
Beyond Isolated Clients: Integrating Graph-Based Embeddings into Event Sequence Models
Apr 10, 2026Harry Proshian, Nikita Severin, Sergey Nikolenko +5Self-Supervised Learning Contrastive Learning
Steklov Institute of Mathematics Saint Petersburg, Russia · Independent Researcher Belgrade, Serbia · ISP RAS Steklov Institute of Mathematics Saint Petersburg, Russia +4
PRISM-CTG: A Foundation Model for Cardiotocography Analysis with Multi-View SSL
Apr 9, 2026Sheng Wong, Ravi Shankar, Beth Albert +5Self-Supervised Time Series Representation Learning Self-Supervised Learning
Nuffield Department of Women’s & Reproductive Health, University of Oxford, United Kingdom · OXDHL, University of Oxford, Oxford, United Kingdom · OATML, Department of Computer Science, University of Oxford, United Kingdom +1
A Self supervised learning framework for imbalanced medical imaging datasets
Apr 2, 2026Yash Kumar Sharma, Charan Ramtej Kodi, Vineet PadmanabhanSelf-Supervised Learning Medical Image Classification
Artificial Intelligence lab, School of Computer & Information Sciences, University of Hyderabad, Hyderabad 500046, India
Mine-JEPA: In-Domain Self-Supervised Learning for Mine-Like Object Classification in Side-Scan Sonar
Apr 1, 2026Taeyoun Kwon, Youngwon Choi, Hyeonyu Kim +3Classification Self-Supervised Learning
Maum AI Inc. · Seoul National University · KAIST +1
Feature-level Site Leakage Reduction for Cross-Hospital Chest X-ray Transfer via Self-Supervised Learning
Mar 31, 2026Ayoub Louaye Bouaziz, Lokmane CheboubaCross-Domain Transfer Learning Cross-Dataset Generalization
University of Mentouri Brothers Constantine 1, Constantine, Algeria · University of Paris-VIII-Vincennes-Saint-Denis, France · LRIA Laboratory, University of Science and Technology Houari Boumediene (USTHB), Bab-Ezzouar, Algeria +1
Learning Concepts, Not Tokens: Self-Supervised Semantic Alignment for Language Models
Mar 31, 2026Christine Zhang, Dan Jurafsky, Chen ShaniLanguage Modeling LLM Alignment
Stanford University
TerraSeg: Self-Supervised Ground Segmentation for Any LiDAR
Mar 28, 2026Ted Lentsch, Santiago Montiel-Marín, Holger Caesar +13D Point Cloud Segmentation Self-Supervised Learning
Department of Cognitive Robotics, Delft University of Technology · Department of Electronics, University of Alcalá
RiskProp: Collision-Anchored Self-Supervised Risk Propagation for Early Accident Anticipation
Mar 28, 2026Yiyang Zou, Tianhao Zhao, Peilun Xiao +9Monotonicity Constraints Traffic Accident Anticipation
School of Computer Science, Wuhan University · Zhongguancun Academy, Beijing, China · Didi Chuxing +1
3D-IDE: 3D Implicit Depth Emergent
Mar 28, 2026Chushan Zhang, Ruihan Lu, Jinguang Tong +23D VLMs 3D Scene Understanding
School of Computing, Australian National University · FreiNexus · School of EECS, The University of Queensland +1

May 2, 2026J/K move · Enter open · S save

MU-SHOT-Fi: Self-Supervised Multi-User Wi-Fi Sensing with Source-free Unsupervised Domain Adaptation

Ahmed Y. Radwan, Hina Tabassum

department of Electrical Engineering and Computer Science, York University, Toronto, ON M3J 1P3, Canada

Source-Free Domain Adaptation Human Activity Recognition Self-Supervised Learning Permutation-Invariant Learning Invariant Representation Learning Wi-Fi Sensing

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Deep learning has been widely adopted for WiFi CSI-based human activity recognition (HAR) due to its ability to learn spatio-temporal features in a privacy-preserving and cost-effective manner. However, DL-based models generalize poorly across environments, a challenge amplified in multi-user settings where overlapping activities cause CSI entanglement and domain shifts. Practical deployments often limit access to labeled source data due to privacy constraints, motivating source-free adaptation using only unlabeled target-domain CSI and a pre-trained source model. In this paper, we propose MU-SHOT-Fi, a source-free unsupervised domain adaptation framework for single- and multi-user Wi-Fi sensing. MU-SHOT-Fi employs permutation-invariant set prediction with Hungarian matching during source training, followed by frozen-classifier backbone adaptation in the target domain. To enable stable adaptation without labels, we introduce occupancy-weighted information maximization that prevents model collapse by focusing diversity regularization on likely-occupied slots while excluding the dominant class from marginal entropy. Additionally, we employ binary rotation prediction as spatial self-supervision that exploits CSI frequency-time structure to learn domain-invariant features. For single-user scenarios, we introduce SU-SHOT-Fi by replacing occupancy weighting with standard information maximization and incorporating contrastive predictive coding to exploit temporal consistency. Extensive experiments on the WiMANS and Widar 3.0 datasets across cross-environment, cross-frequency, cross-orientation, and combined domain shifts demonstrate that MU-SHOT-Fi effectively recovers multi-user exact-activity classification performance under large domain shifts while maintaining accurate occupancy estimation and preventing collapse toward dominant classes.