Research map / Label-efficient and robust learning / Recognition and long-tail
Partial Multi-Label Learning with Label Distribution
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| label enhancement (recovered label distributions) in PML-LD vs. disambiguation baselines PML-LC | PML-LD · scene (avg #CLs=5) | hamming loss: 0.237±0.010 -> 0.119±0.012 | Helped |
| PML-LD vs PARTICLE-VLS on hamming loss, pairwise t-test | PML-LD · 27 PML datasets | hamming loss win/tie/loss: 20/1/6 | Helped |
| PML-LD vs PARTICLE-VLS on one-error, pairwise t-test | PML-LD · 27 PML datasets | one-error win/tie/loss: 1/16/10 | No clear effect |
| PML-LD vs PARTICLE-MAP overall, pairwise t-test | PML-LD · 27 PML datasets, 5 metrics | win/tie/loss: 82/51/2 | Helped |
| PML-LD vs PARTICLE-VLS overall, pairwise t-test | PML-LD · 27 PML datasets, 5 metrics | win/tie/loss: 57/59/19 | Mixed |
| PML-LD vs PML-LC overall, pairwise t-test | PML-LD · 27 PML datasets, 5 metrics | win/tie/loss: 124/11/0 | Helped |
| vary hyperparameters lambda1, lambda2, m, beta1, beta2 | PML-LD · music emotion, music style, eurlex sm | average precision | No clear effect |
Closest papers by meaning
- Partial Multi-Label Learning with Noisy Label Identification (AAAI 2020 · similarity 0.93)
- Partial Multi-Label Learning via Credible Label Elicitation (AAAI 2019 · similarity 0.93)
- Partial Label Learning via Label Enhancement (AAAI 2019 · similarity 0.91)
- Partial Multi-Label Learning (AAAI 2018 · similarity 0.91)
- Realistic Evaluation of Deep Partial-Label Learning Algorithms (ICLR 2025 · similarity 0.89)
- What Makes Partial-Label Learning Algorithms Effective? (NeurIPS 2024 · similarity 0.88)
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