Research map / Label-efficient and robust learning / Recognition and long-tail
End-to-End Probabilistic Label-Specific Feature Learning for Multi-Label Classification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use end-to-end probabilistic label-specific feature learning (PACA) instead of the strongest listed baseline | Multi-label classification · 14 benchmark data sets | Average precision | Mixed |
| Use end-to-end probabilistic label-specific feature learning (PACA) instead of the strongest listed baseline | Multi-label classification · 14 benchmark data sets | Macro-averaging AUC | Helped |
| Model positive and negative label prototypes with multimodal normalizing-flow distributions rather than one Gaussian prototype (ablation) | PACA · 14 benchmark data sets | Average precision: win [0.0001] | Helped |
| Add label embedding-based latent-space regularization (ablation) | PACA · 14 benchmark data sets | Average precision: win [0.0009] | Helped |
| Use PACA rather than MPVAE | Deep multi-label classification · 14 benchmark data sets | Average precision | Mixed |
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