Research map / Label-efficient and robust learning / Domain adaptation and generalization
KL Guided Domain Adaptation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace marginal-alignment baselines with reverse-KL-guided representation alignment | Single-source domain adaptation · RotatedMNIST average (M0 source) | accuracy: 68.6 -> 86.4 | Helped |
| Replace the strongest marginal-alignment baseline with reverse-KL-guided alignment | Single-source domain adaptation · DIGITS average | accuracy: 89.7 -> 96.0 | Helped |
| Replace the strongest marginal-alignment baseline with reverse-KL-guided alignment | Single-source domain adaptation · VisDA17 S→R | accuracy: 62.8±1.1 -> 70.6±0.5 | Helped |
| Replace the strongest marginal-alignment baseline with reverse-KL-guided alignment | Single-source domain adaptation · PACS average | accuracy: 66.3 -> 69.1 | Helped |
| Replace the strongest reported baseline with reverse-KL-guided alignment | Single-source domain adaptation · Office-31 average | accuracy: 82.2 -> 85.3 | Helped |
| Increase batch size from 32 to 64 (ablation) | KL · RotatedMNIST M0→M45 | accuracy: 89.5±0.9 -> 93.3±0.3 | Helped |
| Increase batch size from 64 to 128 (ablation) | KL · RotatedMNIST M0→M45 | accuracy: 93.3±0.3 -> 93.4±1.2 | No clear effect |
| Increase batch size from 128 to 256 (ablation) | KL · RotatedMNIST M0→M45 | accuracy: 93.4±1.2 -> 93.4±0.8 | No clear effect |
| Add the auxiliary forward KL term (ablation) | KL · RotatedMNIST average | accuracy: 83.5 -> 86.4 | Helped |
| Add the auxiliary forward KL term (ablation) | KL · DIGITS average | accuracy: 95.1 -> 96.0 | Helped |
| Add the auxiliary forward KL term (ablation) | KL · VisDA17 S→R | accuracy: 67.8 -> 70.5 | Helped |
Closest papers by meaning
- Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment (ICML 2019 · similarity 0.90)
- Distribution-Informed Neural Networks for Domain Adaptation Regression (NeurIPS 2022 · similarity 0.90)
- A General Representation-Based Approach to Multi-Source Domain Adaptation (ICML 2025 · similarity 0.88)
- Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalization (AAAI 2024 · similarity 0.88)
- Revisiting Source-Free Domain Adaptation: Insights into Representativeness, Generalization, and Variety (CVPR 2025 · similarity 0.87)
- Pseudo-Labeling for Unsupervised Domain Adaptation with Kernel GLMs (NeurIPS 2026 · similarity 0.87)
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