Research map / Label-efficient and robust learning / Recognition and long-tail
Partial Multi-Label Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add confidence-weighted candidate label disambiguation (PML-lc) vs treating all candidates as relevant | multi-label ranking SVM · Emotions | average precision: .625 -> .769 | Helped |
| add confidence-weighted disambiguation (PML-lc) | RankSVM · 7 datasets | Hamming loss: .578 -> .247 (Emotions) | Helped |
| use label-correlation regularizer instead of feature-prototype regularizer (ablation) | PML · Corel5K | average precision: .420 -> .355 | Hurt |
| use PML (linear) vs LIFT | PML-lc · CAL500 | average precision: .581 vs .567 | Hurt |
| vary trade-off C2 (1-10) / C3 (1,10,100) (ablation) | PML-lc / PML-fp · multi-label datasets | all criteria | No clear effect |
Closest papers by meaning
- Partial Multi-Label Learning with Label Distribution (AAAI 2020 · similarity 0.91)
- Partial Multi-Label Learning with Noisy Label Identification (AAAI 2020 · similarity 0.91)
- Distilling Reliable Knowledge for Instance-Dependent Partial Label Learning (AAAI 2024 · similarity 0.88)
- Realistic Evaluation of Deep Partial-Label Learning Algorithms (ICLR 2025 · similarity 0.88)
- What Makes Partial-Label Learning Algorithms Effective? (NeurIPS 2024 · similarity 0.87)
- Conformal Prediction for Partial Label Learning (AAAI 2025 · similarity 0.87)
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