mlsanityData to 14 Oct 2026

DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation

arXiv 1803.10081 ↗First posted 27 Mar 2018cs.CV cs.AIcited by 214

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Abstract (arXiv)

In computer vision, one is often confronted with problems of domain shifts, which occur when one applies a classifier trained on a source dataset to target data sharing similar characteristics (e.g. same classes), but also different latent data structures (e.g. different acquisition conditions). In such a situation, the model will perform poorly on the new data, since the classifier is specialized to recognize visual cues specific to the source domain. In this work we explore a solution, named DeepJDOT, to tackle this problem: through a measure of discrepancy on joint deep representations/labels based on optimal transport, we not only learn new data representations aligned between the source and target domain, but also simultaneously preserve the discriminative information used by the classifier. We applied DeepJDOT to a series of visual recognition tasks, where it compares favorably against state-of-the-art deep domain adaptation methods.

Key facts. Cited by 214 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Mar 2018.

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Citing papers per year: 2021: 46; 2022: 46; 2023: 41; 2024: 34; 2025: 25; 2026: 22.

214 papers.

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  1. 2023Improving and generalizing flow-based generative models with minibatch optimal transportcited by 879
  2. 2023SE(3)-Stochastic Flow Matching for Protein Backbone Generationcited by 150
  3. 2021CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptationcited by 129
  4. 2021Unbalanced minibatch Optimal Transport; applications to Domain Adaptationcited by 125
  5. 2021Predicting with Confidence on Unseen Distributionscited by 116
  6. 2022Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectivescited by 98
  7. 2023Simulation-free Schr"odinger bridges via score and flow matchingcited by 83
  8. 2023LAVA: Data Valuation without Pre-Specified Learning Algorithmscited by 69
  9. 2023Recent Advances in Optimal Transport for Machine Learningcited by 66
  10. 2021Minibatch optimal transport distances; analysis and applicationscited by 65
  11. 2022A Broad Study of Pre-training for Domain Generalization and Adaptationcited by 65
  12. 2021A Prototype-Oriented Framework for Unsupervised Domain Adaptationcited by 62
38 more
  1. 2022Transferability in Deep Learning: A Surveycited by 57
  2. 2022Unified Optimal Transport Framework for Universal Domain Adaptationcited by 57
  3. 2021Rates of Estimation of Optimal Transport Maps using Plug-in Estimators via Barycentric Projectionscited by 53
  4. 2023C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptationcited by 50
  5. 2024Global and Local Prompts Cooperation via Optimal Transport for Federated Learningcited by 46
  6. 2021Online Adaptation to Label Distribution Shiftcited by 44
  7. 2021Federated Multi-Target Domain Adaptationcited by 44
  8. 2021Improving Mini-batch Optimal Transport via Partial Transportationcited by 41
  9. 2022Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Datacited by 36
  10. 2022Neural Optimal Transport with General Cost Functionalscited by 36
  11. 2021A Survey of Unsupervised Domain Adaptation for Visual Recognitioncited by 31
  12. 2022Invariant Causal Mechanisms through Distribution Matchingcited by 31
  13. 2025EgoBridge: Domain Adaptation for Generalizable Imitation from Egocentric Human Datacited by 29
  14. 2024AWT: Transferring Vision-Language Models via Augmentation, Weighting, and Transportationcited by 28
  15. 2023Scalable Optimal Transport Methods in Machine Learning: A Contemporary Surveycited by 27
  16. 2021Conditional Bures Metric for Domain Adaptationcited by 25
  17. 2022Revisiting Sliced Wasserstein on Images: From Vectorization to Convolutioncited by 25
  18. 2022Hierarchical Sliced Wasserstein Distancecited by 23
  19. 2021Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptationcited by 21
  20. 2021TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimationcited by 21
  21. 2023SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptationcited by 21
  22. 2024Your Data Is Not Perfect: Towards Cross-Domain Out-of-Distribution Detection in Class-Imbalanced Datacited by 21
  23. 2022Partially Does It: Towards Scene-Level FG-SBIR with Partial Inputcited by 20
  24. 2022A Unified Framework for Domain Adaptive Pose Estimationcited by 20
  25. 2021Unsupervised Domain Adaptation for LiDAR Panoptic Segmentationcited by 19
  26. 2022A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learningcited by 19
  27. 2021Mapping conditional distributions for domain adaptation under generalized target shiftcited by 18
  28. 2022Learning Distinctive Margin toward Active Domain Adaptationcited by 18
  29. 2022Improving Transferability for Domain Adaptive Detection Transformerscited by 17
  30. 2022Feature Representation Learning for Unsupervised Cross-domain Image Retrievalcited by 17
  31. 2021On Transportation of Mini-batches: A Hierarchical Approachcited by 16
  32. 2021Unsupervised Noise Adaptive Speech Enhancement by Discriminator-Constrained Optimal Transportcited by 16
  33. 2023Sliced Wasserstein Estimation with Control Variatescited by 16
  34. 2023OST: Refining Text Knowledge with Optimal Spatio-Temporal Descriptor for General Video Recognitioncited by 16
  35. 2023Enhancing Diffusion Models with 3D Perspective Geometry Constraintscited by 16
  36. 2021k-Mixup Regularization for Deep Learning via Optimal Transportcited by 14
  37. 2022Collaborative Filtering with Attribution Alignment for Review-based Non-overlapped Cross Domain Recommendationcited by 14
  38. 2022Combating Label Distribution Shift for Active Domain Adaptationcited by 14

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