Research map / Label-efficient and robust learning / Semi-supervised learning
Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use unlabeled data with confidence-rated margin maximization (PARM) instead of label-propagation SSPL | semi-supervised partial label learning · real-world PL datasets | win/tie/loss vs SSPL: 20/21/4 | Helped |
| PARM vs supervised PL-KNN | PARM · real-world PL datasets | win/tie/loss: 45/0/0 | Helped |
| PARM vs supervised CLPL | PARM · real-world PL datasets | win/tie/loss: 31/14/0 | Helped |
| PARM vs PL-AGGD | PARM · real-world PL datasets | win/tie/loss: 24/16/5 | Helped |
| fewer PL training examples (small rho) amplifies PARM advantage | PARM · Satimage | accuracy | Helped |
| vary gamma (ablation) | PARM · BirdSong rho=0.5 | accuracy | No clear effect |
| vary lambda and mu (ablation) | PARM · BirdSong rho=0.5 | accuracy | Mixed |
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