mlsanityData to 7 Oct 2026

AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation

arXiv 2106.04732 ↗First posted 8 Jun 2021cs.LG cs.AI cs.CVcited by 104

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Abstract (arXiv)

We extend semi-supervised learning to the problem of domain adaptation to learn significantly higher-accuracy models that train on one data distribution and test on a different one. With the goal of generality, we introduce AdaMatch, a method that unifies the tasks of unsupervised domain adaptation (UDA), semi-supervised learning (SSL), and semi-supervised domain adaptation (SSDA). In an extensive experimental study, we compare its behavior with respective state-of-the-art techniques from SSL, SSDA, and UDA on vision classification tasks. We find AdaMatch either matches or significantly exceeds the state-of-the-art in each case using the same hyper-parameters regardless of the dataset or task. For example, AdaMatch nearly doubles the accuracy compared to that of the prior state-of-the-art on the UDA task for DomainNet and even exceeds the accuracy of the prior state-of-the-art obtained with pre-training by 6.4% when AdaMatch is trained completely from scratch. Furthermore, by providing AdaMatch with just one labeled example per class from the target domain (i.e., the SSDA setting), we increase the target accuracy by an additional 6.1%, and with 5 labeled examples, by 13.6%.

Key facts. Cited by 104 papers in the map (data through 7 Oct 2026). Builds on 36 of its 63 references that are in the map. First posted on arXiv 8 Jun 2021.

Builds on

36 of 63 references.

References · 36

  1. 2013Intriguing properties of neural networkscited by 5,114
  2. 2017On Calibration of Modern Neural Networkscited by 4,860
  3. 2016Wide Residual Networkscited by 3,057
  4. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  5. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,857
  6. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,080
  7. 2016Identity Mappings in Deep Residual Networkscited by 1,960
  8. 2020The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalizationcited by 1,842
  9. 2019Theoretically Principled Trade-off between Robustness and Accuracycited by 1,510
  10. 2019Do ImageNet Classifiers Generalize to ImageNet?cited by 1,507
  11. 2018ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnesscited by 1,430
  12. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,390
24 more
  1. 2018Moment Matching for Multi-Source Domain Adaptationcited by 1,327
  2. 2019Self-training with Noisy Student improves ImageNet classificationcited by 1,304
  3. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,274
  4. 2016Deep CORAL: Correlation Alignment for Deep Domain Adaptationcited by 1,194
  5. 2019Unsupervised Data Augmentation for Consistency Trainingcited by 1,057
  6. 2016Temporal Ensembling for Semi-Supervised Learningcited by 1,054
  7. 2017Deeper, Broader and Artier Domain Generalizationcited by 983
  8. 2019Big Transfer (BiT): General Visual Representation Learningcited by 766
  9. 2017Maximum Classifier Discrepancy for Unsupervised Domain Adaptationcited by 682
  10. 2014Deep Domain Confusion: Maximizing for Domain Invariancecited by 597
  11. 2019Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learningcited by 507
  12. 2017Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning resultscited by 470
  13. 2017Wild Patterns: Ten Years After the Rise of Adversarial Machine Learningcited by 411
  14. 2019Making Convolutional Networks Shift-Invariant Againcited by 390
  15. 2019Semi-supervised Domain Adaptation via Minimax Entropycited by 333
  16. 2016Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networkscited by 307
  17. 2015Simultaneous Deep Transfer Across Domains and Taskscited by 205
  18. 2018Why do deep convolutional networks generalize so poorly to small image transformations?cited by 198
  19. 2019Generalizing to unseen domains via distribution matchingcited by 109
  20. 2015Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalizationcited by 96
  21. 2014Domain-Adversarial Neural Networkscited by 78
  22. 2019Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalizationcited by 43
  23. 2019Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustnesscited by 18
  24. 2019Using Videos to Evaluate Image Model Robustnesscited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 15; 2023: 35; 2024: 21; 2025: 21; 2026: 7.

104 papers.

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  1. 2022Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistencycited by 246
  2. 2022FreeMatch: Self-adaptive Thresholding for Semi-supervised Learningcited by 232
  3. 2023SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learningcited by 133
  4. 2022USB: A Unified Semi-supervised Learning Benchmark for Classificationcited by 95
  5. 2021Extending the WILDS Benchmark for Unsupervised Adaptationcited by 85
  6. 2023Domain Adaptation for Time Series Under Feature and Label Shiftscited by 60
  7. 2022ProxyMix: Proxy-based Mixup Training with Label Refinery for Source-Free Domain Adaptationcited by 34
  8. 2023RLSbench: Domain Adaptation Under Relaxed Label Shiftcited by 30
  9. 2022Sample Efficiency of Data Augmentation Consistency Regularizationcited by 28
  10. 2021Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detectioncited by 27
  11. 2023Don't Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learnercited by 23
  12. 2023Switching Temporary Teachers for Semi-Supervised Semantic Segmentationcited by 23
38 more
  1. 2022Transferring Fairness under Distribution Shifts via Fair Consistency Regularizationcited by 21
  2. 2022Divide to Adapt: Mitigating Confirmation Bias for Domain Adaptation of Black-Box Predictorscited by 19
  3. 2023On the Over-Memorization During Natural, Robust and Catastrophic Overfittingcited by 19
  4. 2023Rethinking Semi-supervised Learning with Language Modelscited by 17
  5. 2023Semi-Supervised Graph Imbalanced Regressioncited by 14
  6. 2023Partial Identifiability for Domain Adaptationcited by 14
  7. 2023SemiReward: A General Reward Model for Semi-supervised Learningcited by 14
  8. 2023InstanT: Semi-supervised Learning with Instance-dependent Thresholdscited by 13
  9. 2022PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognitioncited by 12
  10. 2022An Embarrassingly Simple Baseline for Imbalanced Semi-Supervised Learningcited by 12
  11. 2023SimMatchV2: Semi-Supervised Learning with Graph Consistencycited by 12
  12. 2024CamoTeacher: Dual-Rotation Consistency Learning for Semi-Supervised Camouflaged Object Detectioncited by 12
  13. 2023Boosting Semi-Supervised Learning by bridging high and low-confidence predictionscited by 11
  14. 2025CGMatch: A Different Perspective of Semi-supervised Learningcited by 11
  15. 2022Boosting Cross-Domain Speech Recognition with Self-Supervisioncited by 10
  16. 2023EEGMatch: Learning with Incomplete Labels for Semi-Supervised EEG-based Cross-Subject Emotion Recognitioncited by 10
  17. 2023Semi-Supervised Dual-Stream Self-Attentive Adversarial Graph Contrastive Learning for Cross-Subject EEG-based Emotion Recognitioncited by 10
  18. 2024Controllable Prompt Tuning For Balancing Group Distributional Robustnesscited by 10
  19. 2025Deep Learning for Time Series Forecasting: A Surveycited by 10
  20. 2025CustomKD: Customizing Large Vision Foundation for Edge Model Improvement via Knowledge Distillationcited by 10
  21. 2021Better Pseudo-label: Joint Domain-aware Label and Dual-classifier for Semi-supervised Domain Generalizationcited by 9
  22. 2023SCoDA: Domain Adaptive Shape Completion for Real Scanscited by 9
  23. 2023Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurationscited by 9
  24. 2023SSB: Simple but Strong Baseline for Boosting Performance of Open-Set Semi-Supervised Learningcited by 9
  25. 2021Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learningcited by 8
  26. 2023How Many Validation Labels Do You Need? Exploring the Design Space of Label-Efficient Model Rankingcited by 8
  27. 2023Flexible Distribution Alignment: Towards Long-tailed Semi-supervised Learning with Proper Calibrationcited by 7
  28. 2024UDA-Bench: Revisiting Common Assumptions in Unsupervised Domain Adaptation Using a Standardized Frameworkcited by 6
  29. 2024RankUp: Boosting Semi-Supervised Regression with an Auxiliary Ranking Classifiercited by 6
  30. 2022Adaptation of Surgical Activity Recognition Models Across Operating Roomscited by 5
  31. 2022SAT: Improving Semi-Supervised Text Classification with Simple Instance-Adaptive Self-Trainingcited by 5
  32. 2025VRM: Knowledge Distillation via Virtual Relation Matchingcited by 5
  33. 2021Holistic Semi-Supervised Approaches for EEG Representation Learningcited by 4
  34. 2023DAC-MR: Data Augmentation Consistency Based Meta-Regularization for Meta-Learningcited by 4
  35. 2023DisCo: Distilled Student Models Co-training for Semi-supervised Text Miningcited by 4
  36. 2023Domain Adaptive Few-Shot Open-Set Learningcited by 4
  37. 2023FLOGA: A machine learning ready dataset, a benchmark and a novel deep learning model for burnt area mapping with Sentinel-2cited by 4
  38. 2023Language Semantic Graph Guided Data-Efficient Learningcited by 4

50 most cited of 104 All 104 (JSON)