Research map / Label-efficient and robust learning / Recognition and long-tail
Multi-Label Manifold Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| exploit reconstructed label manifold (ML2) vs RAKEL | multi-label learning · scene | Hamming loss: 0.096 -> 0.076 | Helped |
| ML2 vs BR | multi-label learning · llog | Hamming loss: 0.017 -> 0.021 | Hurt |
| ML2 vs CLR | multi-label learning · CAL500 | One-error: 0.121 -> 0.141 | Hurt |
| ML2 vs four baselines overall | multi-label learning · regular-scale data sets | rank: ranks 1st in 73.8% cases | Helped |
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