Papers › DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation
DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation
Bharath Bhushan Damodaran, Benjamin Kellenberger, Rémi Flamary, Devis Tuia, Nicolas Courty
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Domain Adaptation | MNIST-to-MNIST-M | DeepJDOT | Accuracy | 92.4 | #2 of 5 | Archive leaderboard | report |
| Domain Adaptation | MNIST-to-USPS | DeepJDOT | Accuracy | 95.7 | #11 of 14 | Archive leaderboard | report |
| Domain Adaptation | SVNH-to-MNIST | DeepJDOT | Accuracy | 96.7 | #5 of 9 | Archive leaderboard | report |
| Domain Adaptation | USPS-to-MNIST | DeepJDOT | Accuracy | 96.4 | #10 of 14 | Archive leaderboard | report |
| Domain Adaptation | VisDA2017 | DeepJDOT | Accuracy | 66.9 | #27 of 28 | Archive leaderboard | report |
| Unsupervised Domain Adaptation | VisDA2017 | DeepJDOT | Accuracy | 66.9 | #12 of 13 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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