Research map / Label-efficient and robust learning / Recognition and long-tail
Partial Multi-Label Learning with Probabilistic Graphical Disambiguation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace heuristic disambiguation (sparsity regularization, PML-NI) with variational graphical disambiguation | PARD · YeastBP | Average precision: 0.430 -> 0.417 | Mixed |
| PARD versus best baselines across real and synthetic data sets | PARD · 25 PML data sets | all five metrics: best in 82.4% of cases | Helped |
| add disambiguation via variational lower bound (vs treating all candidates as valid) (ablation) | linear predictor trained with cross-entropy · real-world PML data sets | Average precision / Ranking loss | Helped |
| vary trade-off parameter alpha (ablation) | PARD · validation sets | performance | No clear effect |
| PARD vs UPML-HL on Hamming loss | PARD · 25 PML data sets | Hamming loss: tie [7.0e-1] | No clear effect |
Closest papers by meaning
- Understanding Partial Multi-Label Learning via Mutual Information (NeurIPS 2021 · similarity 0.92)
- Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning (AAAI 2024 · similarity 0.90)
- Calibrated Disambiguation for Partial Multi-label Learning (AAAI 2025 · similarity 0.89)
- Disentangled Partial Label Learning (AAAI 2024 · similarity 0.89)
- Distilling Reliable Knowledge for Instance-Dependent Partial Label Learning (AAAI 2024 · similarity 0.89)
- Conformal Prediction for Partial Label Learning (AAAI 2025 · similarity 0.88)
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