Semi-Supervised Learning

Also known as SSL

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Overview

Semi-supervised learning studies how to combine a small labeled dataset with a larger unlabeled one, reducing annotation costs in tasks such as image segmentation, speech recognition, and graph classification. Common approaches include pseudo-labeling, teacher–student consistency training, graph-based label propagation, and generative or statistical methods that use unlabeled data to learn structure or improve inference. Current work also adapts these techniques to settings such as open-set recognition, class imbalance, domain shift, and foundation-model tuning. A central challenge is deciding which unlabeled examples and predictions are trustworthy: distribution mismatch, noisy pseudo-labels, and confirmation bias can make additional data harmful, so gains remain dependent on the task and data quality.

Latest papers

CardsList

164 papers13 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

  1. Semi-Supervised Generative Learning via Latent Space Distribution Matching

    Mar 4, 2026Kwong Yu Chong, Long FengConditional Generative ModelingSemi-Supervised Learning

    School of Computing & Data Science, University of Hong Kong

  2. EgoPoseFormer v2: Accurate Egocentric Human Motion Estimation for AR/VR

    Mar 4, 2026Zhenyu Li, Sai Kumar Dwivedi, Filip Maric +11Human Pose Estimation3D Human Pose Estimation

    Meta · KAUST · Max Planck Institute for Intelligent Systems

  3. Task Expansion and Cross Refinement for Open-World Conditional Modeling

    Mar 3, 2026Shreyas Bhat Brahmavar, Qiyang Liu, Yang Li +1Cross-Dataset GeneralizationSemi-Supervised Learning

    Department of Computer Science, University of North Carolina at Chapel Hill · Independent Researcher

  4. Leveraging Label Proportion Prior for Class-Imbalanced Semi-Supervised Learning

    Mar 3, 2026Kohki Akiba, Shinnosuke Matsuo, Shota Harada +1Semi-Supervised LearningClass-Imbalanced Learning

    Kyushu University, Fukuoka, Japan

  5. Relatively Smart: A New Approach for Instance-Optimal Learning

    Mar 2, 2026Shaddin Dughmi, Alireza F. PourPAC LearningSemi-Supervised Learning

    University of Southern California · University of Waterloo

  6. USE: Uncertainty Structure Estimation for Robust Semi-Supervised Learning

    Feb 28, 2026Tsao-Lun Chen, Chien-Liang Liu, Tzu-Ming Harry Hsu +4Semi-Supervised Learning

    National Taiwan University of Science and Technology, Taipei, Taiwan · National Taiwan University, Taipei, Taiwan · Chang Gung University, Taoyuan, Taiwan +3

  7. SOTAlign: Semi-Supervised Alignment of Unimodal Vision and Language Models via Optimal Transport

    Feb 26, 2026Simon Roschmann, Paul Krzakala, Sonia Mazelet +2Cross-Modal LearningMultimodal Embedding

    Helmholtz Munich · Technical University of Munich · Munich Center for Machine Learning +3

  8. Adaptive Semi-Supervised Training of P300 ERP-BCI Speller System with Minimum Calibration Effort

    Feb 17, 2026Shumeng Chen, Jane E. Huggins, Tianwen MaBrain-Computer InterfacesEEG Decoding

    Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, United States · Department of Physical Medicine and Rehabilitation, Department of Biomedical Engineering, University of Michigan, Ann Arbor, United States

  9. Semi-supervised CAPP Transformer Learning via Pseudo-labeling

    Feb 1, 2026Dennis Gross, Helge Spieker, Arnaud Gotlieb +3TransformerPseudo-Labeling

    1- Simula Research Laboratory, Oslo Norway · 2- National Technical University of Athens, School of Mechanical Engineering, Manufacturing Technology Laboratory Greece

  10. Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspective

    Dec 11, 2025Tian Liu, Anwesha Basu, James Caverlee +1VLM AdaptationFew-Shot Learning

    Texas A&M University · University of Macau · Institute of Collaborative Innovation

  11. No Data Wasted: A Semi-supervised Generative Model for Incomplete Multi-view Data Integration with Missing Labels

    Aug 15, 2025Yiyang Shen, Weiran WangLearning with Missing DataMulti-View Learning

    Department of Computer Science, University of Iowa, Iowa City, Iowa, USA

  12. SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation

    Jun 10, 2025Hongjie Zhu, Xiwei Liu, Rundong Xue +5Promptable Image SegmentationSemi-Supervised Medical Image Segmentation

    SWUN · MBZUAI · XJTU +2

  13. Never Skip a Batch: Dense Learning of Temporal GNNs via Adaptive Pseudo-Supervision

    May 18, 2025Alexander Panyshev, Dmitry Vinichenko, Oleg Travkin +2Temporal GNNsTemporal Link Prediction

    Risk AI Research Lab Moscow Independent Research Institute of Artificial Intelligence Moscow, Russia · Risk AI Research Lab Moscow, Russia · Risk AI Research Lab Applied AI Institute Moscow, Russia

  14. Simple yet Effective Semi-supervised Knowledge Distillation from Vision-Language Models via Dual-Head Optimization

    May 12, 2025Seongjae Kang, Dong Bok Lee, Hyungjoon Jang +1Knowledge DistillationVLM Distillation

    VUNO Inc. · KAIST