Research map / Label-efficient and robust learning / Recognition and long-tail
Partial Label Learning with Self-Guided Retraining
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use SURE instead of PLKNN | partial label learning · real-world datasets | classification accuracy | Helped |
| Use SURE instead of the comparison algorithms | partial label learning · real-world datasets | classification accuracy | Mixed |
| Increase the maximum-infinity-norm regularization weight from very small values (ablation) | SURE · Lost | classification accuracy | Helped |
| Make the maximum-infinity-norm regularization weight sufficiently large (ablation) | SURE · Lost | classification accuracy | Hurt |
| Vary the model-complexity parameter to balance overfitting and underfitting (ablation) | SURE · Lost | classification accuracy | Mixed |
Closest papers by meaning
- Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization (NeurIPS 2020 · similarity 0.91)
- Learning with Partial-Label and Unlabeled Data: A Uniform Treatment for Supervision Redundancy and Insufficiency (ICML 2024 · similarity 0.88)
- Reduction-based Pseudo-label Generation for Instance-dependent Partial Label Learning (NeurIPS 2025 · similarity 0.88)
- Controller-Guided Partial Label Consistency Regularization with Unlabeled Data (AAAI 2024 · similarity 0.88)
- Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations (NeurIPS 2024 · similarity 0.87)
- Distilling Reliable Knowledge for Instance-Dependent Partial Label Learning (AAAI 2024 · similarity 0.87)
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