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Optimal Transport for Domain Adaptation

arXiv 1507.00504 ↗First posted 2 Jul 2015cs.LGcited by 457

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Abstract (arXiv)

Domain adaptation from one data space (or domain) to another is one of the most challenging tasks of modern data analytics. If the adaptation is done correctly, models built on a specific data space become more robust when confronted to data depicting the same semantic concepts (the classes), but observed by another observation system with its own specificities. Among the many strategies proposed to adapt a domain to another, finding a common representation has shown excellent properties: by finding a common representation for both domains, a single classifier can be effective in both and use labelled samples from the source domain to predict the unlabelled samples of the target domain. In this paper, we propose a regularized unsupervised optimal transportation model to perform the alignment of the representations in the source and target domains. We learn a transportation plan matching both PDFs, which constrains labelled samples in the source domain to remain close during transport. This way, we exploit at the same time the few labeled information in the source and the unlabelled distributions observed in both domains. Experiments in toy and challenging real visual adaptation examples show the interest of the method, that consistently outperforms state of the art approaches.

Key facts. Cited by 457 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jul 2015.

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Citing papers per year: 2021: 89; 2022: 69; 2023: 75; 2024: 77; 2025: 79; 2026: 68.

457 papers.

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  1. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,550
  2. 2021Unbalanced minibatch Optimal Transport; applications to Domain Adaptationcited by 123
  3. 2021Entropic estimation of optimal transport mapscited by 87
  4. 2021Plugin Estimation of Smooth Optimal Transport Mapscited by 67
  5. 2023Recent Advances in Optimal Transport for Machine Learningcited by 66
  6. 2021Editing a classifier by rewriting its prediction rulescited by 64
  7. 2021Minibatch optimal transport distances; analysis and applicationscited by 62
  8. 2021OTCE: A Transferability Metric for Cross-Domain Cross-Task Representationscited by 62
  9. 2023Patch-Mix Transformer for Unsupervised Domain Adaptation: A Game Perspectivecited by 54
  10. 2021Rates of Estimation of Optimal Transport Maps using Plug-in Estimators via Barycentric Projectionscited by 52
  11. 2021Domain Adversarial Neural Networks for Domain Generalization: When It Works and How to Improvecited by 46
  12. 2022Unbalanced Optimal Transport, from Theory to Numericscited by 45
38 more
  1. 2021Unbalanced Feature Transport for Exemplar-based Image Translationcited by 41
  2. 2021Neural Monge Map estimation and its applicationscited by 40
  3. 2021Improving Mini-batch Optimal Transport via Partial Transportationcited by 40
  4. 2022BMD: A General Class-balanced Multicentric Dynamic Prototype Strategy for Source-free Domain Adaptationcited by 39
  5. 2023Generative Modeling through the Semi-dual Formulation of Unbalanced Optimal Transportcited by 39
  6. 2021Co-Transport for Class-Incremental Learningcited by 38
  7. 2021Automatic Text Evaluation through the Lens of Wasserstein Barycenterscited by 38
  8. 2021Sinkhorn Distributionally Robust Optimizationcited by 38
  9. 2021Few-shot Segmentation with Optimal Transport Matching and Message Flowcited by 36
  10. 2021A Survey on Optimal Transport for Machine Learning: Theory and Applicationscited by 35
  11. 2021Data Efficient Language-supervised Zero-shot Recognition with Optimal Transport Distillationcited by 35
  12. 2022Neural Optimal Transport with General Cost Functionalscited by 34
  13. 2021INTERN: A New Learning Paradigm Towards General Visioncited by 33
  14. 2023PATS: Patch Area Transportation with Subdivision for Local Feature Matchingcited by 33
  15. 2023Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Modelscited by 33
  16. 2022Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyondcited by 32
  17. 2023The Monge Gap: A Regularizer to Learn All Transport Mapscited by 32
  18. 2023Optimal Transport for Treatment Effect Estimationcited by 32
  19. 2021Unbalanced Optimal Transport through Non-negative Penalized Linear Regressioncited by 31
  20. 2022Spherical Sliced-Wassersteincited by 31
  21. 20213D Pose Transfer with Correspondence Learning and Mesh Refinementcited by 30
  22. 2022SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learningcited by 30
  23. 2021Self-Adversarial Disentangling for Specific Domain Adaptationcited by 29
  24. 2023Upcycling Models under Domain and Category Shiftcited by 29
  25. 2021Semi-Supervised Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transportcited by 28
  26. 2022Graph Auto-Encoder Via Neighborhood Wasserstein Reconstructioncited by 28
  27. 2021On Robust Optimal Transport: Computational Complexity and Barycenter Computationcited by 27
  28. 2021Statistical Analysis of Wasserstein Distributionally Robust Estimatorscited by 27
  29. 2021Training for the Future: A Simple Gradient Interpolation Loss to Generalize Along Timecited by 27
  30. 2021Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysiscited by 27
  31. 2023Scalable Optimal Transport Methods in Machine Learning: A Contemporary Surveycited by 27
  32. 2024AWT: Transferring Vision-Language Models via Augmentation, Weighting, and Transportationcited by 27
  33. 2025EgoBridge: Domain Adaptation for Generalizable Imitation from Egocentric Human Datacited by 27
  34. 2025Advancing Sequential Numerical Prediction in Autoregressive Modelscited by 26
  35. 2021Conditional Bures Metric for Domain Adaptationcited by 25
  36. 2024LEAD: Learning Decomposition for Source-free Universal Domain Adaptationcited by 25
  37. 2022Domain Adaptation for Time-Series Classification to Mitigate Covariate Shiftcited by 24
  38. 2021Improving Neural Cross-Lingual Summarization via Employing Optimal Transport Distance for Knowledge Distillationcited by 22

50 most cited of 457 All 457 (JSON)