Research map / Label-efficient and robust learning / Recognition and long-tail
PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add classification training after self-supervised prototype pretraining (ablation) | PIP-Net R · CUB-200-2011 | test prototype purity: 0.29 ± 0.32 -> 0.65 ± 0.25 | Helped |
| add classification training after self-supervised prototype pretraining (ablation) | PIP-Net C · CUB-200-2011 | test prototype purity: 0.60 ± 0.38 -> 0.93 ± 0.15 | Helped |
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