Research map / Label-efficient and robust learning / Domain adaptation and generalization
Discrepancy-Based Active Learning for Domain Adaptation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Select target queries by minimizing distances to the labeled source-and-query set using K-medoids, rather than the strongest listed baseline | K-medoids active learning · Superconductivity, 12 source→target domain pairs | MAE | Mixed |
| Select target queries using K-medoids rather than K-means | Superconductivity active learning · Superconductivity l→h | MAE: 13.98 vs 14.11 | Hurt |
| Select queries by spatial coverage rather than uncertainty sampling | Superconductivity active learning · Superconductivity, reported domain pairs | MAE | Helped |
| Account for distances to already-labeled source points when selecting queries | K-medoids versus K-means · Superconductivity, reported domain pairs | MAE | Helped |
Closest papers by meaning
- On $f$-Divergence Principled Domain Adaptation: An Improved Framework (NeurIPS 2024 · similarity 0.88)
- Efficient Discrepancy Testing for Learning with Distribution Shift (NeurIPS 2024 · similarity 0.86)
- Active Domain Adaptation with False Negative Prediction for Object Detection (CVPR 2024 · similarity 0.86)
- Category-Aware Active Domain Adaptation (ICML 2024 · similarity 0.86)
- Understanding and Improving Source-free Domain Adaptation from a Theoretical Perspective (CVPR 2024 · similarity 0.86)
- Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer (CVPR 2024 · similarity 0.85)
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