mlsanityData to 7 Oct 2026

Unsupervised Domain Adaptation via Regularized Conditional Alignment

arXiv 1905.10885 ↗First posted 26 May 2019cs.LG cs.CV stat.MLcited by 34

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Abstract (arXiv)

We propose a method for unsupervised domain adaptation that trains a shared embedding to align the joint distributions of inputs (domain) and outputs (classes), making any classifier agnostic to the domain. Joint alignment ensures that not only the marginal distributions of the domain are aligned, but the labels as well. We propose a novel objective function that encourages the class-conditional distributions to have disjoint support in feature space. We further exploit adversarial regularization to improve the performance of the classifier on the domain for which no annotated data is available.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 May 2019.

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Citing papers per year: 2021: 12; 2022: 10; 2023: 6; 2024: 3; 2025: 2; 2026: 1.

34 papers.

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  1. 2022Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptationcited by 93
  2. 2021Domain Impression: A Source Data Free Domain Adaptation Methodcited by 91
  3. 2021Semantic Concentration for Domain Adaptationcited by 42
  4. 2021Specificity-Preserving Federated Learning for MR Image Reconstructioncited by 35
  5. 2022TeST: Test-time Self-Training under Distribution Shiftcited by 29
  6. 2021Generation, augmentation, and alignment: A pseudo-source domain based method for source-free domain adaptationcited by 28
  7. 2022Gradual Domain Adaptation without Indexed Intermediate Domainscited by 26
  8. 2024Test-Time Adaptation for Depth Completioncited by 26
  9. 2024Pairwise Alignment Improves Graph Domain Adaptationcited by 25
  10. 2021Leveraging Self-Supervision for Cross-Domain Crowd Countingcited by 24
  11. 2022DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentationcited by 22
  12. 2022Online Domain Adaptation for Semantic Segmentation in Ever-Changing Conditionscited by 16
22 more
  1. 2021Multi-Source domain adaptation via supervised contrastive learning and confident consistency regularizationcited by 12
  2. 2023A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain Adaptationcited by 11
  3. 2021A Curriculum-style Self-training Approach for Source-Free Semantic Segmentationcited by 10
  4. 2024Reshaping the Online Data Buffering and Organizing Mechanism for Continual Test-Time Adaptationcited by 9
  5. 2021Robust Domain-Free Domain Generalization with Class-aware Alignmentcited by 6
  6. 2022Domain Adaptation with Adversarial Training on Penultimate Activationscited by 5
  7. 2023Unsupervised Domain Adaptation via Distilled Discriminative Clusteringcited by 5
  8. 2025Progressive Test Time Energy Adaptation for Medical Image Segmentationcited by 5
  9. 2021Adaptive Pseudo-Label Refinement by Negative Ensemble Learning for Source-Free Unsupervised Domain Adaptationcited by 4
  10. 2022Low-confidence Samples Matter for Domain Adaptationcited by 4
  11. 2025ETA: Energy-based Test-time Adaptation for Depth Completioncited by 4
  12. 2021Unsupervised Domain Adaptation for Image Classification via Structure-Conditioned Adversarial Learningcited by 3
  13. 2021Vicinal and categorical domain adaptationcited by 3
  14. 2023Relating tSNE and UMAP to Classical Dimensionality Reductioncited by 3
  15. 2022OneRing: A Simple Method for Source-free Open-partial Domain Adaptationcited by 2
  16. 2023Contrast and Clustering: Learning Neighborhood Pair Representation for Source-free Domain Adaptationcited by 2
  17. 2021On the inductive biases of deep domain adaptationcited by 1
  18. 2022Cycle Label-Consistent Networks for Unsupervised Domain Adaptationcited by 1
  19. 2022Consecutive Knowledge Meta-Adaptation Learning for Unsupervised Medical Diagnosiscited by 1
  20. 2023DAFD: Domain Adaptation via Feature Disentanglement for Image Classificationcited by 1
  21. 2023Rectifying Group Irregularities in Explanations for Distribution Shiftcited by 1
  22. 2026Learning Fair Domain Adaptation with Virtual Label Distributioncited by 0