Semi-Supervised Learning

Also known as SSL

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

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

Should Pseudo-Labels Share Mini-Batches with Labeled Examples? Semi-Supervised Continual Active Learning with Limited Training Cost
Oct 26, 2026Shiqi Zhang, Tuomas VirtanenActive Learning Pseudo-Labeling
Faculty of Information Technology and Communication Sciences Tampere University, Tampere, Finland
ReLEAP: Upcycling Vision Datasets through Cross-Task Unification and Semi-Supervised Annotation
Oct 23, 2026Julia Simon, Daniel Steininger, Andreas TrondlCross-Dataset Generalization Semi-Supervised Learning
AIT Austrian Institute of Technology, Giefinggasse 4, 1210 Vienna, Austria Center for Vision, Automation & Control
Multi-Objective Evolutionary Semi-Supervised Learning for Resource-Aware Smart City Classification
Oct 20, 2026Francisco José Sedeño, Jamal ToutouhSemi-Supervised Learning Tabular Classification
ITIS Software, University of Malaga, Malaga, Spain
Semi-supervised learning on singular data sets
Oct 18, 2026Guillaume ValetteSemi-Supervised Learning Point Cloud Learning
Not All Unlabeled Data Are Equal: Unlabeled Data Selection for Semi-Supervised QSAR Modeling with Large Chemical Repositories
Oct 15, 2026Oliver Majer, Saso Dzeroski, Jurica LevaticTraining Data Selection Semi-Supervised Learning
Jožef Stefan Institute, Jamova cesta 39, Ljubljana, Slovenia · University of Ljubljana, Faculty of Computer and Information Science, Večna pot 113, Ljubljana, Slovenia
Uncertainty-Aware Semi-Supervised Framework with Supervised Contrastive Learning for Sugarcane Leaf Disease Detection
Oct 13, 2026Shalini Dangi, Divya Teja, Omkar Kabde +2Plant Disease Classification Semi-Supervised Learning
Indian Institute of Technology Indore, Madhya Pradesh, India · Chaitanya Bharathi Institute of Technology, Telangana, India
CoGameReg: Cooperative-Game-Based Collaborative Semi-Supervised Regression for Seismic Reservoir Property Prediction
Oct 9, 2026Zhiyu Guo, Jiwei Jia, Lei LiSemi-Supervised Learning Ensemble Learning
School of Mathematics, Jilin University, Changchun, China · AI for Science and Engineering Center, Shenzhen Loop Area Institute, Shenzhen, China · BGP Inc., China National Petroleum Corporation, Zhuozhou, China +1
Decoupled Optimization for Teacher-Student Semi-Supervised Learning via a Pioneer Student
Oct 7, 2026Haorong Han, Jidong Yuan, Chixuan Wei +1Semi-Supervised Learning Teacher-Student Learning
School of Computer Science and Technology Beijing Jiaotong University · School of Data Science and Intelligent Media Communication University of China
Label-Efficient Deep Learning for ECG Delineation: A Multi-Dataset Benchmark against Widely Used Delineation Tools
Oct 6, 2026Jeonghwa Lim, Minje Park, Yeongyeon Na +6Self-Supervised Learning Electrocardiography
VUNO Inc., Seoul, South Korea · Department of AI Mobility Engineering, Ajou University, Suwon, South Korea · C&Thoth Co., Ltd., Seoul, South Korea +3
Structure-aware Keypoint Localization for Videofluoroscopic Swallowing Study
Oct 6, 2026Kai Zhou, Chuanshen Chen, Runhao Zeng +6Keypoint Detection Semi-Supervised Learning
South China University of Technology · Shenzhen MSU-BIT University · The Third Affiliated Hospital of Sun Yat-sen University +1
Hub for Outliers, Spokes for Inliers: Uniform Latent Space Construction for Dual-Mismatched Semi-Supervised Learning
Oct 6, 2026Li Yuan, Yaxin Hou, Jiawei Tang +2Class-Imbalanced Learning Semi-Supervised Learning
School of Computer Science and Engineering, Southeast University, Nanjing, China
Range-GRPO: Policy Optimization via Pairwise Relations among Reward Intervals
Oct 1, 2026Ryunyi Lee, Kangjun Noh, Somin Kim +2Reward Modeling LLM-as-a-Judge
Yonsei University
SpecRegMatch: Robust Semi-Supervised Regression for Vehicle Interior Noise Prediction
Sep 28, 2026Sejin Sim, Jinsoo Bae, Seoung Bum KimSemi-Supervised Learning
School of Industrial and Management Engineering, Korea University, Seoul 02841, South Korea
WhiteCon: Semi-Supervised Domain Adaptation Regression Through Whitening Transform and Dual Consistency
Sep 28, 2026Se Jin Sim, Seoung Bum KimDomain Adaptation Feature Whitening
School of Industrial and Management Engineering Korea University, Seoul, Republic of Korea
Geometric Inductive Biases for Semi-Supervised Equalization: The Constellation-Aware Transformer
Sep 27, 2026Avi CaciularuTransformer Wireless Communications
Google Research
AnchorMixGAN: Anchor-Aligned Generative Semi-Supervision for DDoS Detection in Cloud-Integrated IoT Networks
Sep 26, 2026Jin Yang, Xufeng Liu, Yong Hu +3Network Intrusion Detection Cybersecurity
School of Cyber Science and Engineering, Sichuan University, Chengdu, 610207, China · College of Information Science and Technology, Xizang University, Lhasa, 850000, China · Shaanxi Key Laboratory of Intelligent Policing, Shaanxi Police College, Xi’an, 710021, China +2
Label Less, Learn More: Resource-Efficient Active Semi-Supervised Learning for Onboard Satellite Image Annotation
Sep 25, 2026Ahmed Abdelnaby, Mohamed Elmahallawy, Marius Bernahrndt +1Data-Efficient Learning Semi-Supervised Learning
School of Engineering and Applied Science, Washington State University, Richland, WA 99354, USA · Institute for Software Technology, German Aerospace Center (DLR), Porz, 51147 Cologne, Germany
Colon3R: Cross-Domain 3D Reconstruction from Monocular Colonoscopic Video
Sep 21, 2026Zhihao Xing, Yingyu Wang, Liang Zhao +1Domain Adaptation 3D Reconstruction
Learning Submanifolds for Subsequent Inference on Random Dot Product Graphs, Part 1: Theory
Sep 16, 2026Michael W. Trosset, Carey E. PriebeSemi-Supervised Learning Decision-Making under Uncertainty
Department of Statistics, Indiana University. · Department of Applied Mathematics & Statistics, Johns Hopkins University.
Pseudo-Label Augmentation for Affect Sensing in Small Collaborative Groups
Sep 13, 2026Meisam Jamshidi Seikavandi, Tanya Ignatenko, Fabricio Batista Narcizo +3Affective Computing Pseudo-Labeling
IT University of Copenhagen brAIn Lab Copenhagen, Denmark · GN Advanced Science, GN Hearing Ballerup, Denmark · GN Advanced Science, GN Hearing Glenview, Illinois, United States
Follow the Geometry, Not the Model: Cold Start Semi-Supervised Learning
Sep 13, 2026Itai David, Daphna WeinshallSemi-Supervised Learning Graph-Based Semi-Supervised Learning
School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
Relatively Smart II: Tractable or Semi-Supervised Instance-Optimal Learning
Sep 9, 2026Shaddin Dughmi, Alireza F. PourClassification Semi-Supervised Learning
University of Southern California · University of Waterloo
Unlabeled Echoes: Pseudo-Labels and Genus-Aware Smoothing for Bat Call Recognition
Sep 8, 2026Frank Fundel, Alexandra HowardPseudo-Labeling Bioacoustics
Ludwig-Maximilians-Universität München, Germany · University of the Free State, Department of Zoology and Entomology, Qwaqwa Campus, South Africa
Semi-Supervised Learning under Spatially Biased Sampling
Sep 7, 2026Bright Wiredu Nuakoh, Francky Fouedjio, Stephen Bradshaw +4Distribution Shift Semi-Supervised Learning
African Institute for Mathematical Sciences (AIMS), 17 KN 16 Ave, Kigali, Rwanda · Mathematical Application & Data Analytics Group, School of Science, Edith Cowan University, 270 Joondalup Drive, Joondalup, WA 6027, Australia · Data & Analytics, Rio Tinto, 152-158 St Georges Terrace, Perth, WA 6000, Australia +2
Generalizable Brain Tumor Segmentation with Self-Training and Tumor-Aware Deformations
Sep 2, 2026Henrique Zan Grande, Jeovane Honorio Alves, Rayson Laroca +1Brain Tumor Segmentation BraTS
Pontifícia Universidade Católica do Paraná (PUCPR), Programa de Pós-Graduação em Informática (PPGIa), Curitiba, Paraná, Brazil · University of Luxembourg, SEDAN - SnT, Luxembourg, Luxembourg
Semi-Supervised Classification with Informative Missing Labels in Weibull Mixture Models
Sep 1, 2026Jinran Wu, You-Gan Wang, Geoffrey J. McLachlanLearning with Missing Data Binary Classification
The University of Queensland, Australia
Learning Dynamics of Logits Debiasing for Long-Tailed Semi-Supervised Learning
Aug 31, 2026Yue Cheng, Jiajun Zhang, Xiaohui Gao +2Class-Imbalanced Learning Semi-Supervised Learning
Beijing Jiaotong University · AntGroup · Northwestern Polytechnical University +1
ReCalMatch:Reliability-Calibrated Semantic Guidance for Semi-Supervised Fine-Grained Recognition
Aug 30, 2026Yundi Hong, Hongyang He, Zheng Fang +2Semi-Supervised Learning Fine-Grained Image Classification
Department of Computer Science University of Warwick Coventry, UK · Universitat Autonoma de Barcelona, Spain
Classifier-Dependent Benefits of Pseudo-Labeling for Semi-Supervised Android Malware Attribution
Aug 26, 2026Md Rafid Islam, Zahid Hasan, Hafiz Abdur RahmanPseudo-Labeling Malware Classification
Department of Electrical and Computer Engineering North South University Dhaka, Bangladesh
Semi-Supervised Adaptation of Vision-Language Models for Image Classification
Aug 26, 2026Mohamed L. Mekhalfi, Mohamad M. Al Rahhal, Yakoub Bazi +4Remote Sensing Scene Classification VLM Adaptation
Fondazione Bruno Kessler, Via Sommarive 18, 38123, Povo TN, Italy · Applied Computer Science Department, College of Applied Computer Science, King Saud University, 11543, Riyadh, Saudi Arabia · Computer Engineering Department, College of Computer and Information Sciences, King Saud University, 11543, Riyadh, Saudi Arabia +2

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

Should Pseudo-Labels Share Mini-Batches with Labeled Examples? Semi-Supervised Continual Active Learning with Limited Training Cost

Shiqi Zhang, Tuomas Virtanen

Faculty of Information Technology and Communication Sciences Tampere University, Tampere, Finland

Active Learning Pseudo-Labeling Semi-Supervised Learning

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Active learning (AL) reduces annotation by querying informative examples. Continual active learning (CAL) also cuts the training cost, the total number of gradient steps, by training one model across AL rounds with a fixed number of mini-batch steps per round. Pseudo-labels add supervision without annotation, but they increase training cost. In CAL, where every round trains on a fixed number of mini-batches, whether and how pseudo-labeled examples should share these mini-batches with labeled examples is therefore open. We provide the first formalization of this problem as two decisions after each query, namely (i) which unlabeled examples should enter training with a pseudo-label and (ii) what share of each mini-batch they should take. Our method, semi-supervised continual active learning (SSCAL), works with any acquisition function. It admits pseudo-labels based on distances from unlabeled examples to labeled neighbors, caps their share, and reweights the loss on labeled examples to keep their expected gradient contribution unchanged. On frozen embeddings, its pseudo-labeler updates incrementally without extra model forward passes. Across two audio datasets, two frozen encoders, six acquisition functions and ten seeds, SSCAL ranks first on average among four protocols with equal training steps, answering the question affirmatively.