Research map / Label-efficient and robust learning / Domain adaptation and generalization
Domain Adaptation as a Problem of Inference on Graphical Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use graphical-model inference (Infer) instead of DICA | multi-source domain adaptation · Simulated, 2 sources | accuracy (%): 86.56 -> 93.10 | Helped |
| use Infer instead of LMP with 4 sources | multi-source domain adaptation · Simulated, 4 sources | accuracy (%): 79.06 -> 85.38 | Helped |
| use Infer instead of Soft-Max | multi-source domain adaptation · Wi-Fi t1,t3->t2 | accuracy (%): 44.95 -> 84.5 | Helped |
| use Infer instead of poolNN | multi-source domain adaptation · GvHD flow, 3 sources | accuracy (%): 95.7 -> 96.8 | Helped |
| use Infer instead of Soft-Max on MNIST target | multi-source domain adaptation · Digits S+M+D/T | accuracy (%): 97.9 -> 96.64 | Hurt |
| use Infer instead of Soft-Max on MNIST-M target | multi-source domain adaptation · Digits T+S+D/M | accuracy (%): 68.7 -> 89.89 | Helped |
| use Infer instead of poolNN_Ours (same classifier architecture) | multi-source domain adaptation · Digits M+T+D/S | accuracy (%): 67.8 -> 89.34 | Helped |
Closest papers by meaning
- Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions (NeurIPS 2018 · similarity 0.92)
- Domain Adaptation with Conditional Transferable Components (ICML 2016 · similarity 0.89)
- A General Representation-Based Approach to Multi-Source Domain Adaptation (ICML 2025 · similarity 0.88)
- Revisiting Source-Free Domain Adaptation: Insights into Representativeness, Generalization, and Variety (CVPR 2025 · similarity 0.88)
- Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer (CVPR 2024 · similarity 0.88)
- Adapting to Distribution Shift by Visual Domain Prompt Generation (ICLR 2024 · similarity 0.87)
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