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Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation

arXiv 1903.04064 ↗First posted 10 Mar 2019cs.CV cs.LG stat.MLcited by 235

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Abstract (arXiv)

In this work, we connect two distinct concepts for unsupervised domain adaptation: feature distribution alignment between domains by utilizing the task-specific decision boundary and the Wasserstein metric. Our proposed sliced Wasserstein discrepancy (SWD) is designed to capture the natural notion of dissimilarity between the outputs of task-specific classifiers. It provides a geometrically meaningful guidance to detect target samples that are far from the support of the source and enables efficient distribution alignment in an end-to-end trainable fashion. In the experiments, we validate the effectiveness and genericness of our method on digit and sign recognition, image classification, semantic segmentation, and object detection.

Key facts. Cited by 235 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Mar 2019.

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Citing papers per year: 2021: 71; 2022: 55; 2023: 45; 2024: 34; 2025: 23; 2026: 7.

235 papers.

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  1. 2021Domain Generalization: A Surveycited by 760
  2. 2021Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentationcited by 268
  3. 2022Contrastive Test-Time Adaptationcited by 258
  4. 2021Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptationcited by 176
  5. 2021Generalized Source-free Domain Adaptationcited by 175
  6. 2021Self-supervised Augmentation Consistency for Adapting Semantic Segmentationcited by 149
  7. 2022Causality Inspired Representation Learning for Domain Generalizationcited by 136
  8. 2021CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptationcited by 129
  9. 2022Domain Adaptation via Prompt Learningcited by 128
  10. 2021Predicting with Confidence on Unseen Distributionscited by 116
  11. 2021Source-free Domain Adaptation via Avatar Prototype Generation and Adaptationcited by 111
  12. 2023A Comprehensive Survey on Source-free Domain Adaptationcited by 104
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  1. 2022Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectivescited by 98
  2. 2022Source-Free Domain Adaptation via Distribution Estimationcited by 97
  3. 2022Visual Prompt Tuning for Test-time Domain Adaptationcited by 97
  4. 2021Unsupervised Domain Adaptation of Object Detectors: A Surveycited by 93
  5. 2022Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptationcited by 93
  6. 2021PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Trainingcited by 80
  7. 2022Source-Free Unsupervised Domain Adaptation: A Surveycited by 79
  8. 2022Deep Clustering: A Comprehensive Surveycited by 78
  9. 2021Cross-domain Contrastive Learning for Unsupervised Domain Adaptationcited by 75
  10. 2021Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptationcited by 67
  11. 2022Reusing the Task-specific Classifier as a Discriminator: Discriminator-free Adversarial Domain Adaptationcited by 66
  12. 2021Category Contrast for Unsupervised Domain Adaptation in Visual Taskscited by 65
  13. 2021MixStyle Neural Networks for Domain Generalization and Adaptationcited by 64
  14. 2023Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptationcited by 63
  15. 2021A Prototype-Oriented Framework for Unsupervised Domain Adaptationcited by 62
  16. 2022Safe Self-Refinement for Transformer-based Domain Adaptationcited by 61
  17. 2021HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learningcited by 57
  18. 2022Transferability in Deep Learning: A Surveycited by 57
  19. 2021Transferable Semantic Augmentation for Domain Adaptationcited by 56
  20. 2023Patch-Mix Transformer for Unsupervised Domain Adaptation: A Game Perspectivecited by 54
  21. 2022Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learningcited by 53
  22. 2023C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptationcited by 49
  23. 2021Point-set Distances for Learning Representations of 3D Point Cloudscited by 48
  24. 2021A Theory of Label Propagation for Subpopulation Shiftcited by 48
  25. 2021RDA: Robust Domain Adaptation via Fourier Adversarial Attackingcited by 48
  26. 2021ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Imagescited by 48
  27. 2021Self-Supervised Noisy Label Learning for Source-Free Unsupervised Domain Adaptationcited by 47
  28. 2022Uncertainty-guided Source-free Domain Adaptationcited by 46
  29. 2023When Source-Free Domain Adaptation Meets Learning with Noisy Labelscited by 46
  30. 2021Federated Multi-Target Domain Adaptationcited by 44
  31. 2021Multi-Target Domain Adaptation with Collaborative Consistency Learningcited by 43
  32. 2021Cross-View Regularization for Domain Adaptive Panoptic Segmentationcited by 41
  33. 2022BMD: A General Class-balanced Multicentric Dynamic Prototype Strategy for Source-free Domain Adaptationcited by 39
  34. 2023Energy-Based Sliced Wasserstein Distancecited by 39
  35. 2021Cross-category Video Highlight Detection via Set-based Learningcited by 38
  36. 2022Task-specific Inconsistency Alignment for Domain Adaptive Object Detectioncited by 37
  37. 2021Contrastive Domain Adaptationcited by 36
  38. 2022ProxyMix: Proxy-based Mixup Training with Label Refinery for Source-Free Domain Adaptationcited by 34

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