Research map / Label-efficient and robust learning / Domain adaptation and generalization
Dynamic Weighted Learning for Unsupervised Domain Adaptation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use dynamic weighting of domain alignment and class discrimination losses | DWL · VisDA-2017 | mean accuracy (%): 75.9 -> 77.1 | Helped |
| Use dynamic weighting of domain alignment and class discrimination losses | DWL · Digits | average accuracy (%): 96.9 -> 97.6 | Helped |
| Use dynamic weighting of domain alignment and class discrimination losses | DWL · Office-31 | average accuracy (%): 86.2 -> 87.1 | Helped |
| Use dynamic weighting of domain alignment and class discrimination losses | DWL · ImageCLEF-DA | average accuracy (%): 89.7 -> 90.5 | Helped |
| Add sample weighting to the domains (ablation) | DWL on Office-31 W→A without τ · Office-31 W→A | accuracy (%): 67.9 -> 68.3 | Helped |
| Add dynamic balancing factor τ (ablation) | DWL on Office-31 W→A without sample weighting · Office-31 W→A | accuracy (%): 67.9 -> 69.1 | Helped |
| Add both sample weighting and dynamic balancing factor τ (ablation) | DWL on Office-31 W→A · Office-31 W→A | accuracy (%): 67.9 -> 69.8 | Helped |
| Replace static loss weighting with dynamic τ weighting (ablation) | DWL · Digits M→U | accuracy (%): 93.8, 96.2, 94.6, 94.5, 94.9 -> 97.3 | Helped |
| Replace static loss weighting with dynamic τ weighting (ablation) | DWL · Digits U→M | accuracy (%): 96.8, 95.6, 96.8, 95.6, 95.6 -> 97.4 | Helped |
Closest papers by meaning
- Progressive Feature Alignment for Unsupervised Domain Adaptation (CVPR 2019 · similarity 0.90)
- Make the U in UDA Matter: Invariant Consistency Learning for Unsupervised Domain Adaptation (NeurIPS 2023 · similarity 0.90)
- Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation (CVPR 2017 · similarity 0.89)
- Unsupervised Domain Adaptation Using Feature-Whitening and Consensus Loss (CVPR 2019 · similarity 0.89)
- Guiding Pseudo-Labels With Uncertainty Estimation for Source-Free Unsupervised Domain Adaptation (CVPR 2023 · similarity 0.89)
- Self-Training with Dynamic Weighting for Robust Gradual Domain Adaptation (NeurIPS 2025 · similarity 0.88)
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