Research map / Label-efficient and robust learning / Domain adaptation and generalization
Denoised Maximum Classifier Discrepancy for Source-Free Unsupervised Domain Adaptation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply D-MCD instead of CPGA | ResNet-101 · VISDA | accuracy: 86.0 -> 87.5 | Helped |
| Apply D-MCD instead of SHOT | ResNet-50 · Office-Home | accuracy: 71.8 -> 72.2 | Helped |
| Apply D-MCD instead of CPGA | ResNet-50 · Office31 | accuracy: 89.9 -> 89.9 | No clear effect |
| Add model adaptation before self-training (ablation) | D-MCD · VISDA | accuracy: 74.2 -> 87.5 | Helped |
| Add matching between high- and low-confidence sample distributions (ablation) | D-MCD · VISDA | accuracy: 86.1 -> 87.5 | Helped |
| Add the strong-weak self-training paradigm (ablation) | D-MCD · VISDA | accuracy: 83.9 -> 87.5 | Helped |
Closest papers by meaning
- Understanding and Improving Source-free Domain Adaptation from a Theoretical Perspective (CVPR 2024 · similarity 0.92)
- Revisiting Source-Free Domain Adaptation: Insights into Representativeness, Generalization, and Variety (CVPR 2025 · similarity 0.90)
- On $f$-Divergence Principled Domain Adaptation: An Improved Framework (NeurIPS 2024 · similarity 0.89)
- Pseudo-Labeling for Unsupervised Domain Adaptation with Kernel GLMs (NeurIPS 2026 · similarity 0.89)
- Gradient-Based Sample Selection for Black-Box Universal Domain Adaptation (AAAI 2025 · similarity 0.88)
- Proxy Denoising for Source-Free Domain Adaptation (ICLR 2025 · similarity 0.88)
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