Research map / Label-efficient and robust learning / Domain adaptation and generalization
MOT: Masked Optimal Transport for Partial Domain Adaptation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace existing OT-based PDA methods with MOT | PDA models · VisDA-2017 | accuracy: 98.4 vs 95.9 | Helped |
| Replace existing OT-based PDA methods with MOT | PDA models · ImageCLEF | accuracy: 93.6 vs 90.5 | Helped |
| Use the proposed relaxed and reweighted OT formulation rather than reweighted OT (ablation) | MOT PDA model · PDA ablation (ResNet-50) | accuracy: 73.9 -> 80.6 | Helped |
| Use the proposed relaxed and reweighted OT formulation rather than unbalanced OT (ablation) | MOT PDA model · PDA ablation (ResNet-50) | accuracy: 69.1 -> 80.6 | Helped |
| Add conditional masked OT to the relaxed and reweighted formulation (ablation) | MOT PDA model · PDA ablation (ResNet-50) | accuracy: 74.2 -> 80.6 | Helped |
| Add transported-source risk minimization and conditional alignment (ablation) | MOT PDA model · PDA ablation (ResNet-50) | accuracy: 74.6 -> 80.6 | Helped |
Closest papers by meaning
- Probability-Polarized Optimal Transport for Unsupervised Domain Adaptation (AAAI 2024 · similarity 0.88)
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- Theoretical Performance Guarantees for Partial Domain Adaptation via Partial Optimal Transport (ICML 2025 · similarity 0.85)
- Overcoming Spurious Solutions in Semi-Dual Neural Optimal Transport: A Smoothing Approach for Learning the Optimal Transport Plan (ICML 2025 · similarity 0.85)
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