mlsanityData to 7 Oct 2026

A Brief Review of Domain Adaptation

arXiv 2010.03978 ↗First posted 7 Oct 2020cs.LG cs.CVcited by 287

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Abstract (arXiv)

Classical machine learning assumes that the training and test sets come from the same distributions. Therefore, a model learned from the labeled training data is expected to perform well on the test data. However, This assumption may not always hold in real-world applications where the training and the test data fall from different distributions, due to many factors, e.g., collecting the training and test sets from different sources, or having an out-dated training set due to the change of data over time. In this case, there would be a discrepancy across domain distributions, and naively applying the trained model on the new dataset may cause degradation in the performance. Domain adaptation is a sub-field within machine learning that aims to cope with these types of problems by aligning the disparity between domains such that the trained model can be generalized into the domain of interest. This paper focuses on unsupervised domain adaptation, where the labels are only available in the source domain. It addresses the categorization of domain adaptation from different viewpoints. Besides, It presents some successful shallow and deep domain adaptation approaches that aim to deal with domain adaptation problems.

Key facts. Cited by 287 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Oct 2020.

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Citing papers per year: 2021: 12; 2022: 32; 2023: 60; 2024: 76; 2025: 71; 2026: 36.

287 papers.

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  11. 2023Meta-learning approaches for few-shot learning: A survey of recent advancescited by 25
  12. 2023APPLeNet: Visual Attention Parameterized Prompt Learning for Few-Shot Remote Sensing Image Generalization using CLIPcited by 24
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  4. 2023ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessmentcited by 19
  5. 2022No Parameter Left Behind: How Distillation and Model Size Affect Zero-Shot Retrievalcited by 17
  6. 2023Unlearnable Examples Give a False Sense of Security: Piercing through Unexploitable Data with Learnable Examplescited by 17
  7. 2023Data-Driven Design for Metamaterials and Multiscale Systems: A Reviewcited by 17
  8. 2024SA-GDA: Spectral Augmentation for Graph Domain Adaptationcited by 16
  9. 2024Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual Recognitioncited by 16
  10. 2024A Short Survey on Importance Weighting for Machine Learningcited by 15
  11. 2024PatchRefiner: Leveraging Synthetic Data for Real-Domain High-Resolution Monocular Metric Depth Estimationcited by 15
  12. 2023Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Surveycited by 14
  13. 2023Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Timecited by 14
  14. 2025Handling Out-of-Distribution Data: A Surveycited by 14
  15. 2021A Concise Review of Transfer Learningcited by 13
  16. 2023Unsupervised Domain Adaption with Pixel-level Discriminator for Image-aware Layout Generationcited by 13
  17. 2024Layout Agnostic Human Activity Recognition in Smart Homes through Textual Descriptions Of Sensor Triggers (TDOST)cited by 13
  18. 2024STAMP: Outlier-Aware Test-Time Adaptation with Stable Memory Replaycited by 13
  19. 2025Uni-Retrieval: A Multi-Style Retrieval Framework for STEM's Educationcited by 13
  20. 2024Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Surveycited by 12
  21. 2022Towards out of distribution generalization for problems in mechanicscited by 11
  22. 2023Adaptive Training Distributions with Scalable Online Bilevel Optimizationcited by 11
  23. 2025Evolution and The Knightian Blindspot of Machine Learningcited by 11
  24. 2022Improving Automated Program Repair with Domain Adaptationcited by 10
  25. 2023AGI for Agriculturecited by 9
  26. 2023Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data Scarcitycited by 9
  27. 2023Visual Data-Type Understanding does not emerge from Scaling Vision-Language Modelscited by 9
  28. 2024Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histologycited by 9
  29. 2025Examining and Adapting Time for Multilingual Classification via Mixture of Temporal Expertscited by 9
  30. 2025TimeStep Master: Asymmetrical Mixture of Timestep LoRA Experts for Versatile and Efficient Diffusion Models in Visioncited by 9
  31. 2025Generalizable Domain Adaptation for Sim-and-Real Policy Co-Trainingcited by 9
  32. 2022Billions of Parameters Are Worth More Than In-domain Training Data: A case study in the Legal Case Entailment Taskcited by 8
  33. 2022Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicinecited by 8
  34. 2023Exploring Causal Learning through Graph Neural Networks: An In-depth Reviewcited by 8
  35. 2024Counterfactual Fairness by Combining Factual and Counterfactual Predictionscited by 8
  36. 2022MiDAS: Multi-integrated Domain Adaptive Supervision for Fake News Detectioncited by 7
  37. 2022Cross-Mode Knowledge Adaptation for Bike Sharing Demand Prediction using Domain-Adversarial Graph Neural Networkscited by 7
  38. 2022Mixture Domain Adaptation to Improve Semantic Segmentation in Real-World Surveillancecited by 7

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