Research map / Label-efficient and robust learning / Zero-/few-shot and meta-learning
Open-Set Likelihood Maximization for Few-Shot Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Include and optimize latent inlierness scores to down-weight likely outliers (ablation) | OSLO · mini-ImageNet 1-shot | AUROC: 64.97 -> 74.92 | Helped |
| Include and optimize latent inlierness scores to down-weight likely outliers (ablation) | OSLO · mini-ImageNet 1-shot | Acc: 69.42 -> 71.73 | Helped |
| Run optimization instead of using the initialization (ablation) | OSLO · mini-ImageNet 1-shot | Acc: 66.63 -> 71.73 | Helped |
| Run optimization instead of using the initialization (ablation) | OSLO · mini-ImageNet 1-shot | AUROC: 71.76 -> 74.92 | Helped |
| Use transductive classification without explicit open-set modeling | transductive classifiers · mini-ImageNet and tiered-ImageNet FSOSR | AUROC: 52.45; 49.96; 50.00; 50.35 | Mixed |
Closest papers by meaning
- Few-Shot Open-Set Recognition by Transformation Consistency (CVPR 2021 · similarity 0.88)
- Open-Set Recognition: A Good Closed-Set Classifier is All You Need (ICLR 2022 · similarity 0.88)
- The Devil is in the Wrongly-classified Samples: Towards Unified Open-set Recognition (ICLR 2023 · similarity 0.88)
- Meta Evidential Transformer for Few-Shot Open-Set Recognition (ICML 2024 · similarity 0.88)
- Transductive Zero-Shot and Few-Shot CLIP (CVPR 2024 · similarity 0.86)
- FSL-Rectifier: Rectify Outliers in Few-Shot Learning via Test-Time Augmentation (AAAI 2025 · similarity 0.86)
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