mlsanityData to 14 Oct 2026

Unsupervised Domain Adaptation: A Reality Check

arXiv 2111.15672 ↗First posted 30 Nov 2021cs.CVcited by 24

Not on the map. Why?

Abstract (arXiv)

Interest in unsupervised domain adaptation (UDA) has surged in recent years, resulting in a plethora of new algorithms. However, as is often the case in fast-moving fields, baseline algorithms are not tested to the extent that they should be. Furthermore, little attention has been paid to validation methods, i.e. the methods for estimating the accuracy of a model in the absence of target domain labels. This is despite the fact that validation methods are a crucial component of any UDA train/val pipeline. In this paper, we show via large-scale experimentation that 1) in the oracle setting, the difference in accuracy between UDA algorithms is smaller than previously thought, 2) state-of-the-art validation methods are not well-correlated with accuracy, and 3) differences between UDA algorithms are dwarfed by the drop in accuracy caused by validation methods.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). Builds on 34 of its 52 references that are in the map. First posted on arXiv 30 Nov 2021.

Builds on

34 of 52 references.

References · 34

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  7. 2015Learning Transferable Features with Deep Adaptation Networkscited by 1,329
  8. 2017Adversarial Discriminative Domain Adaptationcited by 1,311
  9. 2017Deep Hashing Network for Unsupervised Domain Adaptationcited by 1,188
  10. 2020Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptationcited by 848
  11. 2017Maximum Classifier Discrepancy for Unsupervised Domain Adaptationcited by 683
  12. 2016Deep Transfer Learning with Joint Adaptation Networkscited by 612
22 more
  1. 2015Return of Frustratingly Easy Domain Adaptationcited by 391
  2. 2016Unsupervised Domain Adaptation with Residual Transfer Networkscited by 341
  3. 2019Semi-supervised Domain Adaptation via Minimax Entropycited by 335
  4. 2019Adversarial Domain Adaptation with Domain Mixupcited by 250
  5. 2019Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptationcited by 235
  6. 2018A DIRT-T Approach to Unsupervised Domain Adaptationcited by 215
  7. 2015Simultaneous Deep Transfer Across Domains and Taskscited by 206
  8. 2019Domain-Specific Batch Normalization for Unsupervised Domain Adaptationcited by 197
  9. 2017Generate To Adapt: Aligning Domains using Generative Adversarial Networkscited by 183
  10. 2017Asymmetric Tri-training for Unsupervised Domain Adaptationcited by 152
  11. 2018Open Set Domain Adaptation by Backpropagationcited by 150
  12. 2020Neural Unsupervised Domain Adaptation in NLP---A Surveycited by 144
  13. 2020Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situationscited by 133
  14. 2020Gradually Vanishing Bridge for Adversarial Domain Adaptationcited by 130
  15. 2018Partial Adversarial Domain Adaptationcited by 127
  16. 2019Domain-Symmetric Networks for Adversarial Domain Adaptationcited by 120
  17. 2020Dual Mixup Regularized Learning for Adversarial Domain Adaptationcited by 99
  18. 2020Unsupervised Domain Adaptation in Semantic Segmentation: a Reviewcited by 88
  19. 2020SENTRY: Selective Entropy Optimization via Committee Consistency for Unsupervised Domain Adaptationcited by 78
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  22. 2021Adversarial Branch Architecture Search for Unsupervised Domain Adaptationcited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 6; 2023: 4; 2024: 8; 2025: 3; 2026: 3.

24 papers.

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  1. 2022OpenGait: Revisiting Gait Recognition Toward Better Practicalitycited by 85
  2. 2022Do Current Multi-Task Optimization Methods in Deep Learning Even Help?cited by 60
  3. 2022Predicting Out-of-Distribution Error with the Projection Normcited by 50
  4. 2023Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregationcited by 15
  5. 2024OpenGait: A Comprehensive Benchmark Study for Gait Recognition towards Better Practicalitycited by 15
  6. 2022Three New Validators and a Large-Scale Benchmark Ranking for Unsupervised Domain Adaptationcited by 13
  7. 2024A Change Detection Reality Checkcited by 13
  8. 2024Align and Distill: Unifying and Improving Domain Adaptive Object Detectioncited by 12
  9. 2025Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluationcited by 10
  10. 2023Better Practices for Domain Adaptationcited by 9
  11. 2025Consensus-Driven Active Model Selectioncited by 8
  12. 2025SIMSHIFT: A Benchmark for Adapting Neural Surrogates to Distribution Shiftscited by 7
12 more
  1. 2024UDA-Bench: Revisiting Common Assumptions in Unsupervised Domain Adaptation Using a Standardized Frameworkcited by 6
  2. 2023Meta Omnium: A Benchmark for General-Purpose Learning-to-Learncited by 4
  3. 2024Crafting Distribution Shifts for Validation and Training in Single Source Domain Generalizationcited by 4
  4. 2023Deep Unsupervised Domain Adaptation for Time Series Classification: a Benchmarkcited by 3
  5. 20223D Reconstruction of Sculptures from Single Images via Unsupervised Domain Adaptation on Implicit Modelscited by 2
  6. 2024SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalitiescited by 2
  7. 2024Parameter Choice and Neuro-Symbolic Approaches for Deep Domain-Invariant Learningcited by 1
  8. 2022A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methodscited by 0
  9. 2024Objective and subjective evaluation of speech enhancement methods in the UDASE task of the 7th CHiME challengecited by 0
  10. 2026Stabilizing Test-Time Adaptation of High-Dimensional Simulation Surrogates via D-Optimal Statisticscited by 0
  11. 2026Towards Practical Algorithm Selection for Unsupervised Domain Adaptation in Medical Imagingcited by 0
  12. 2026How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imagingcited by 0