mlsanityData to 14 Oct 2026

Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives

arXiv 2208.07422 ↗First posted 15 Aug 2022cs.CV cs.AI cs.LGcited by 98

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Abstract (arXiv)

Deep learning has become the method of choice to tackle real-world problems in different domains, partly because of its ability to learn from data and achieve impressive performance on a wide range of applications. However, its success usually relies on two assumptions: (i) vast troves of labeled datasets are required for accurate model fitting, and (ii) training and testing data are independent and identically distributed. Its performance on unseen target domains, thus, is not guaranteed, especially when encountering out-of-distribution data at the adaptation stage. The performance drop on data in a target domain is a critical problem in deploying deep neural networks that are successfully trained on data in a source domain. Unsupervised domain adaptation (UDA) is proposed to counter this, by leveraging both labeled source domain data and unlabeled target domain data to carry out various tasks in the target domain. UDA has yielded promising results on natural image processing, video analysis, natural language processing, time-series data analysis, medical image analysis, etc. In this review, as a rapidly evolving topic, we provide a systematic comparison of its methods and applications. In addition, the connection of UDA with its closely related tasks, e.g., domain generalization and out-of-distribution detection, has also been discussed. Furthermore, deficiencies in current methods and possible promising directions are highlighted.

Key facts. Cited by 98 papers in the map (data through 14 Oct 2026). Builds on 183 of its 315 references that are in the map. First posted on arXiv 15 Aug 2022.

Builds on

183 of 315 references.

References · 183

  1. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  2. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
  3. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  4. 2015Domain-Adversarial Training of Neural Networkscited by 3,044
  5. 2016Improved Techniques for Training GANscited by 2,970
  6. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  7. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  8. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,917
  9. 2020Don't Stop Pretraining: Adapt Language Models to Domains and Taskscited by 1,565
  10. 2018Group Normalizationcited by 1,524
  11. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,485
  12. 2018Moment Matching for Multi-Source Domain Adaptationcited by 1,331
38 more
  1. 2015Learning Transferable Features with Deep Adaptation Networkscited by 1,329
  2. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,328
  3. 2017Adversarial Discriminative Domain Adaptationcited by 1,311
  4. 2016Deep CORAL: Correlation Alignment for Deep Domain Adaptationcited by 1,200
  5. 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,133
  6. 2017CyCADA: Cycle-Consistent Adversarial Domain Adaptationcited by 1,082
  7. 2018Deep Anomaly Detection with Outlier Exposurecited by 942
  8. 2016Playing for Data: Ground Truth from Computer Gamescited by 818
  9. 2017Maximum Classifier Discrepancy for Unsupervised Domain Adaptationcited by 683
  10. 2018Deep Visual Domain Adaptation: A Surveycited by 635
  11. 2016Deep Transfer Learning with Joint Adaptation Networkscited by 612
  12. 2014Deep Domain Confusion: Maximizing for Domain Invariancecited by 600
  13. 2018Learning to Adapt Structured Output Space for Semantic Segmentationcited by 584
  14. 2018Domain Adaptive Faster R-CNN for Object Detection in the Wildcited by 443
  15. 2017VisDA: The Visual Domain Adaptation Challengecited by 435
  16. 2019Dreaming to Distill: Data-free Knowledge Transfer via DeepInversioncited by 423
  17. 2018A Survey on Deep Transfer Learningcited by 419
  18. 2019Confidence Regularized Self-Trainingcited by 419
  19. 2019Contrastive Adaptation Network for Unsupervised Domain Adaptationcited by 407
  20. 2017Semantic Foggy Scene Understanding with Synthetic Datacited by 403
  21. 2015Return of Frustratingly Easy Domain Adaptationcited by 391
  22. 2017Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samplescited by 389
  23. 2018Detecting and Correcting for Label Shift with Black Box Predictorscited by 366
  24. 2015A Recurrent Latent Variable Model for Sequential Datacited by 362
  25. 2018A Survey of Unsupervised Deep Domain Adaptationcited by 361
  26. 2018How Does Batch Normalization Help Optimization?cited by 357
  27. 2021Domain Adaptation for Medical Image Analysis: A Surveycited by 352
  28. 2016Unsupervised Domain Adaptation with Residual Transfer Networkscited by 341
  29. 2019Semi-supervised Domain Adaptation via Minimax Entropycited by 335
  30. 2016Domain Separation Networkscited by 320
  31. 2017DualGAN: Unsupervised Dual Learning for Image-to-Image Translationcited by 310
  32. 2016Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networkscited by 308
  33. 2016Revisiting Batch Normalization For Practical Domain Adaptationcited by 288
  34. 2016Learning from Simulated and Unsupervised Images through Adversarial Trainingcited by 288
  35. 2017Joint Distribution Optimal Transportation for Domain Adaptationcited by 282
  36. 2017Learning to Discover Cross-Domain Relations with Generative Adversarial Networkscited by 278
  37. 2018Strong-Weak Distribution Alignment for Adaptive Object Detectioncited by 270
  38. 2019Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentationcited by 260

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Citing papers per year: 2022: 2; 2023: 24; 2024: 25; 2025: 34; 2026: 13.

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  1. 2023A Comprehensive Survey on Source-free Domain Adaptationcited by 104
  2. 2023Domain Adaptation for Time Series Under Feature and Label Shiftscited by 60
  3. 2023Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitalscited by 25
  4. 2024Any-Shift Prompting for Generalization over Distributionscited by 24
  5. 2024Protected Test-Time Adaptation via Online Entropy Matching: A Betting Approachcited by 23
  6. 2023MADG: Margin-based Adversarial Learning for Domain Generalizationcited by 22
  7. 2024Are NeRFs ready for autonomous driving? Towards closing the real-to-simulation gapcited by 20
  8. 2023Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanomacited by 18
  9. 2023Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Surveycited by 14
  10. 2024Exploring the Benefits of Vision Foundation Models for Unsupervised Domain Adaptationcited by 11
  11. 2025Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluationcited by 10
  12. 2023Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRIcited by 9
38 more
  1. 2025Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target Datacited by 9
  2. 2022Mixture Domain Adaptation to Improve Semantic Segmentation in Real-World Surveillancecited by 7
  3. 2023Domain-Generalizable Multiple-Domain Clusteringcited by 7
  4. 2024DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Seriescited by 7
  5. 2025Toward Robust Non-Transferable Learning: A Survey and Benchmarkcited by 7
  6. 2024Domain Generalization with Small Datacited by 6
  7. 2025Text-to-SQL Domain Adaptation via Human-LLM Collaborative Data Annotationcited by 6
  8. 2023Emo-DNA: Emotion Decoupling and Alignment Learning for Cross-Corpus Speech Emotion Recognitioncited by 5
  9. 2024Domain adaptation in small-scale and heterogeneous biological datasetscited by 5
  10. 2024Resolving Domain Shift For Representations Of Speech In Non-Invasive Brain Recordingscited by 5
  11. 2022Domain Adaptation and Generalization on Functional Medical Images: A Systematic Surveycited by 4
  12. 2024Learning from Unlabelled Data with Transformers: Domain Adaptation for Semantic Segmentation of High Resolution Aerial Imagescited by 4
  13. 2024Unsupervised Domain Adaptation Architecture Search with Self-Training for Land Cover Mappingcited by 4
  14. 2025Mixture-of-Shape-Experts (MoSE): End-to-End Shape Dictionary Framework to Prompt SAM for Generalizable Medical Segmentationcited by 4
  15. 2025Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detectioncited by 4
  16. 2023ASPEST: Bridging the Gap Between Active Learning and Selective Predictioncited by 3
  17. 2024Detecting Domain Shift in Multiple Instance Learning for Digital Pathology Using Fr'echet Domain Distancecited by 3
  18. 2024SiamSeg: Self-Training with Contrastive Learning for Unsupervised Domain Adaptation Semantic Segmentation in Remote Sensingcited by 3
  19. 2025Label Calibration in Source Free Domain Adaptationcited by 3
  20. 2026Unsupervised Confidence Calibration for Reasoning LLMs from a Single Generationcited by 3
  21. 2023Graph Harmony: Denoising and Nuclear-Norm Wasserstein Adaptation for Enhanced Domain Transfer in Graph-Structured Datacited by 2
  22. 2023Attentive Continuous Generative Self-training for Unsupervised Domain Adaptive Medical Image Translationcited by 2
  23. 2023Robust Stance Detection: Understanding Public Perceptions in Social Mediacited by 2
  24. 2023Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentationcited by 2
  25. 2024DART: A Principled Approach to Adversarially Robust Unsupervised Domain Adaptationcited by 2
  26. 2024Incremental Pseudo-Labeling for Black-Box Unsupervised Domain Adaptationcited by 2
  27. 2024Is user feedback always informative? Retrieval Latent Defending for Semi-Supervised Domain Adaptation without Source Datacited by 2
  28. 2024Neural Network Prediction of Strong Lensing Systems with Domain Adaptation and Uncertainty Quantificationcited by 2
  29. 2025SIDDA: SInkhorn Dynamic Domain Adaptation for Image Classification with Equivariant Neural Networkscited by 2
  30. 2023Watch Where You Head: A View-biased Domain Gap in Gait Recognition and Unsupervised Adaptationcited by 1
  31. 2023Domain Adaptation for Satellite-Borne Hyperspectral Cloud Detectioncited by 1
  32. 2023Learning from SAM: Harnessing a Foundation Model for Sim2Real Adaptation by Regularizationcited by 1
  33. 2023Weakly-semi-supervised object detection in remotely sensed imagerycited by 1
  34. 2024Speech motion anomaly detection via cross-modal translation of 4D motion fields from tagged MRIcited by 1
  35. 2024A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspectivecited by 1
  36. 2025Deep Learning-Enhanced Visual Monitoring in Hazardous Underwater Environments with a Swarm of Micro-Robotscited by 1
  37. 2025JurisCTC: Enhancing Legal Judgment Prediction via Cross-Domain Transfer and Contrastive Learningcited by 1
  38. 2025Fault Detection in New Wind Turbines with Limited Data by Generative Transfer Learningcited by 1

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