Research map / Label-efficient and robust learning / Domain adaptation and generalization
LTF: A Label Transformation Framework for Correcting Label Shift
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Label transformation framework (LTF) adaptation instead of KMM density-ratio matching | continuous target shift synthetic Moon data · Moon synthetic, Shifts A-D | MSE of prediction: Ours lower than KMM in all shift settings | Helped |
| Matching in pre-trained feature space h(X) (feature matching) vs raw input (ablation) | KMM baseline on Moon synthetic · Moon synthetic, Shifts A-D | MSE of prediction: KMM(feature) slightly better than KMM in some settings, not others | Mixed |
Closest papers by meaning
- RLSbench: Domain Adaptation Under Relaxed Label Shift (ICML 2023 · similarity 0.88)
- ELSA: Efficient Label Shift Adaptation through the Lens of Semiparametric Models (ICML 2023 · similarity 0.88)
- Heterogeneous Label Shift: Theory and Algorithm (ICML 2025 · similarity 0.87)
- Directional Label Diffusion Model for Learning from Noisy Labels (CVPR 2025 · similarity 0.86)
- Learning Label Shift Correction for Test-Agnostic Long-Tailed Recognition (ICML 2024 · similarity 0.86)
- Generative Calibration of Inaccurate Annotation for Label Distribution Learning (AAAI 2024 · similarity 0.86)
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