mlsanityData to 14 Oct 2026

OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with Outliers

arXiv 2105.14148 ↗First posted 28 May 2021cs.CVcited by 43

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Abstract (arXiv)

Semi-supervised learning (SSL) is an effective means to leverage unlabeled data to improve a model's performance. Typical SSL methods like FixMatch assume that labeled and unlabeled data share the same label space. However, in practice, unlabeled data can contain categories unseen in the labeled set, i.e., outliers, which can significantly harm the performance of SSL algorithms. To address this problem, we propose a novel Open-set Semi-Supervised Learning (OSSL) approach called OpenMatch. Learning representations of inliers while rejecting outliers is essential for the success of OSSL. To this end, OpenMatch unifies FixMatch with novelty detection based on one-vs-all (OVA) classifiers. The OVA-classifier outputs the confidence score of a sample being an inlier, providing a threshold to detect outliers. Another key contribution is an open-set soft-consistency regularization loss, which enhances the smoothness of the OVA-classifier with respect to input transformations and greatly improves outlier detection. OpenMatch achieves state-of-the-art performance on three datasets, and even outperforms a fully supervised model in detecting outliers unseen in unlabeled data on CIFAR10.

Key facts. Cited by 43 papers in the map (data through 14 Oct 2026). Builds on 31 of its 48 references that are in the map. First posted on arXiv 28 May 2021.

Builds on

31 of 48 references.

References · 31

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  3. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  4. 2016Wide Residual Networkscited by 3,070
  5. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  6. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,269
  7. 2013Describing Textures in the Wildcited by 2,125
  8. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,085
  9. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,391
  10. 2020Big Self-Supervised Models are Strong Semi-Supervised Learnerscited by 1,380
  11. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,355
  12. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,328
19 more
  1. 2019Unsupervised Data Augmentation for Consistency Trainingcited by 1,060
  2. 2016Temporal Ensembling for Semi-Supervised Learningcited by 1,057
  3. 2018Deep Anomaly Detection with Outlier Exposurecited by 942
  4. 2019Using Self-Supervised Learning Can Improve Model Robustness and Uncertaintycited by 514
  5. 2017Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning resultscited by 472
  6. 2016Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learningcited by 441
  7. 2020CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instancescited by 441
  8. 2019ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoringcited by 349
  9. 2019S4L: Self-Supervised Semi-Supervised Learningcited by 335
  10. 2018Recent Advances in Open Set Recognition: A Surveycited by 310
  11. 2019Interpolation Consistency Training for Semi-Supervised Learningcited by 251
  12. 2018Learning Confidence for Out-of-Distribution Detection in Neural Networkscited by 245
  13. 2020Contrastive Training for Improved Out-of-Distribution Detectioncited by 186
  14. 2018Open Set Domain Adaptation by Backpropagationcited by 150
  15. 2021OVANet: One-vs-All Network for Universal Domain Adaptationcited by 92
  16. 2020Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learningcited by 82
  17. 2015Distributional Smoothing with Virtual Adversarial Trainingcited by 61
  18. 2020Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networkscited by 31
  19. 2021On Data-Augmentation and Consistency-Based Semi-Supervised Learningcited by 13

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 8; 2023: 20; 2024: 7; 2025: 5; 2026: 1.

43 papers.

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  1. 2022FreeMatch: Self-adaptive Thresholding for Semi-supervised Learningcited by 232
  2. 2022USB: A Unified Semi-supervised Learning Benchmark for Classificationcited by 95
  3. 2023Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discoverycited by 72
  4. 2022Semi-Supervised and Unsupervised Deep Visual Learning: A Surveycited by 52
  5. 2022OpenCon: Open-world Contrastive Learningcited by 33
  6. 2023Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sourcescited by 31
  7. 2023Learning on Graphs with Out-of-Distribution Nodescited by 29
  8. 2023Incremental Generalized Category Discoverycited by 25
  9. 2023IOMatch: Simplifying Open-Set Semi-Supervised Learning with Joint Inliers and Outliers Utilizationcited by 23
  10. 2021OpenCoS: Contrastive Semi-supervised Learning for Handling Open-set Unlabeled Datacited by 22
  11. 2023A Graph-Theoretic Framework for Understanding Open-World Semi-Supervised Learningcited by 22
  12. 2024Robust Semi-supervised Learning by Wisely Leveraging Open-set Datacited by 20
31 more
  1. 2022Open-Set Semi-Supervised Object Detectioncited by 17
  2. 2025DebGCD: Debiased Learning with Distribution Guidance for Generalized Category Discoverycited by 15
  3. 2024Effective Prompt Pool Learning for Continual Category Discoverycited by 14
  4. 2021SSR: An Efficient and Robust Framework for Learning with Unknown Label Noisecited by 13
  5. 2023InstanT: Semi-supervised Learning with Instance-dependent Thresholdscited by 13
  6. 2023InPL: Pseudo-labeling the Inliers First for Imbalanced Semi-supervised Learningcited by 12
  7. 2023Coreset Sampling from Open-Set for Fine-Grained Self-Supervised Learningcited by 12
  8. 2022Fix-A-Step: Semi-supervised Learning from Uncurated Unlabeled Datacited by 11
  9. 2023RoPAWS: Robust Semi-supervised Representation Learning from Uncurated Datacited by 10
  10. 2022Knowledge Distillation Meets Open-Set Semi-Supervised Learningcited by 9
  11. 2023Systematic comparison of semi-supervised and self-supervised learning for medical image classificationcited by 8
  12. 2023Semi-Supervised Learning via Weight-aware Distillation under Class Distribution Mismatchcited by 8
  13. 2024Robust Semi-Supervised Learning in Open Environmentscited by 8
  14. 2023Out-of-Distributed Semantic Pruning for Robust Semi-Supervised Learningcited by 7
  15. 2023Reliability-Adaptive Consistency Regularization for Weakly-Supervised Point Cloud Segmentationcited by 5
  16. 2022Open-Set Semi-Supervised Learning for 3D Point Cloud Understandingcited by 3
  17. 2024Multimodal Unsupervised Domain Generalization by Retrieving Across the Modality Gapcited by 3
  18. 2023Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Datacited by 2
  19. 2023Exploration and Exploitation of Unlabeled Data for Open-Set Semi-Supervised Learningcited by 2
  20. 2025Unsupervised Learning for Class Distribution Mismatchcited by 2
  21. 2025PartCo: Part-Level Correspondence Priors Enhance Category Discoverycited by 2
  22. 2023Semi-Supervised Learning in the Few-Shot Zero-Shot Scenariocited by 1
  23. 2023Prototype Fission: Closing Set for Robust Open-set Semi-supervised Learningcited by 1
  24. 2023Detecting and Learning Out-of-Distribution Data in the Open world: Algorithm and Theorycited by 1
  25. 2024Open-World Semi-Supervised Learning for Node Classificationcited by 1
  26. 2025Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with Outlierscited by 1
  27. 2023OpenIncrement: A Unified Framework for Open Set Recognition and Deep Class-Incremental Learningcited by 0
  28. 2024InterLUDE: Interactions between Labeled and Unlabeled Data to Enhance Semi-Supervised Learningcited by 0
  29. 2024Effects of Common Regularization Techniques on Open-Set Recognitioncited by 0
  30. 2025Open-Set Semi-Supervised Learning for Long-Tailed Medical Datasetscited by 0
  31. 2026Geometric Gradient Rectification for Safe Open-Set Semi-Supervised Learningcited by 0