Research map / Label-efficient and robust learning / Recognition and long-tail
Collaboration Based Multi-Label Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Learn label correlations by sparse reconstruction and use them to explicitly couple final predictions during training | CAMEL · Regular-size datasets (16 datasets × 7 metrics) | cases outperforming BR, ECC, and RAKEL: 93.8% (315/336) cases | Helped |
| Learn label correlations and explicitly correlate final predictions rather than using similarity-based hypothesis regularization | CAMEL · Compared with LLSF and JFSC across datasets and metrics | cases outperforming LLSF and JFSC: 80.4% (180/224) cases | Helped |
| Vary the collaboration degree α (ablation) | CAMEL · enron | Coverage | Mixed |
| Vary the model-complexity parameter λ₂ (ablation) | CAMEL · enron | Coverage | Mixed |
| Vary the tradeoff parameter λ₁ (ablation) | CAMEL · enron | Coverage | No clear effect |
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- In Pursuit of Causal Label Correlations for Multi-label Image Recognition (NeurIPS 2024 · similarity 0.89)
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- Addressing Multi-Label Learning with Partial Labels: From Sample Selection to Label Selection (AAAI 2025 · similarity 0.89)
- Generalization Analysis for Multi-Label Learning (ICML 2024 · similarity 0.89)
- Multi-Label Supervised Contrastive Learning (AAAI 2024 · similarity 0.87)
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