Research map / Label-efficient and robust learning / Recognition and long-tail
Partial Label Learning via Label Influence Function
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Label influence function-based ground-truth identification (PLL-IF) with NN classifier | PLL-IF+NN vs baselines · six real-world PLL datasets | accuracy | Helped |
| Label influence function-based disambiguation with linear SVM classifier | PLL-IF+SVM vs non-neural baselines · six real-world PLL datasets | accuracy | Mixed |
| Label influence function-based disambiguation with NN classifier | PLL-IF+NN vs PL-BLC on synthetic UCI datasets · synthetic UCI PLL (162 datasets) | win/tie/loss count: 150 wins, 8 ties, 4 losses vs PL-BLC overall is not stated; PLL-IF+NN vs PL-BLC total 135 wins, 16 ties, 11 losses | Helped |
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