Research map / Label-efficient and robust learning / Zero-/few-shot and meta-learning
Zero-Shot Learning With Attribute Selection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add attribute selection (AS) | CMT · AwA2/aPY/SUN/CUB | accuracy: 35.10 -> 39.55 average | Helped |
| add attribute selection (AS) | ESZSL · AwA2/aPY/SUN/CUB | accuracy: 51.33 -> 55.46 average | Helped |
| add attribute selection (AS) | ALE · CUB | accuracy: 54.9 -> 54.81(−0.09) | No clear effect |
| add attribute selection (AS) | SJE · SUN | accuracy: 53.7 -> 53.77(+0.07) | No clear effect |
| add attribute selection (AS) | six ZSL approaches · four benchmarks | average accuracy: +2.79% average | Helped |
| remove 5-20% of attributes (low information/predictability) (ablation) | CMT/ESZSL/LatEm · four benchmarks | accuracy | Helped |
| remove 30-50% of attributes (ablation) | CMT/ESZSL/LatEm · four benchmarks | accuracy | Hurt |
| joint optimization with ZSL model (HAS/SAS) instead of naive attribute selection (ablation) | attribute selection · four benchmarks | accuracy | Helped |
| soft (SAS) vs hard (HAS) attribute selection (ablation) | attribute selection · four benchmarks | accuracy | Mixed |
Closest papers by meaning
- Make an Omelette with Breaking Eggs: Zero-Shot Learning for Novel Attribute Synthesis (NeurIPS 2022 · similarity 0.92)
- Task Aligned Generative Meta-learning for Zero-shot Learning (AAAI 2021 · similarity 0.91)
- Attribute Propagation Network for Graph Zero-Shot Learning (AAAI 2020 · similarity 0.90)
- Boosting Generative Zero-Shot Learning by Synthesizing Diverse Features with Attribute Augmentation (AAAI 2022 · similarity 0.90)
- Data-Free Generalized Zero-Shot Learning (AAAI 2024 · similarity 0.89)
- Data Distribution Distilled Generative Model for Generalized Zero-Shot Recognition (AAAI 2024 · similarity 0.88)
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