Research map / Label-efficient and robust learning / Zero-/few-shot and meta-learning
Meta-Learning for Generalized Zero-Shot Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add MAML-style meta-learning to WGAN generator and discriminator (ablation) | conditional WGAN · CUB ZSL | accuracy: 59.1% -> 69.7% | Helped |
| add meta-learning to WGAN (ablation) | conditional WGAN · AWA2 ZSL | accuracy: 68.1% -> 77.5% | Helped |
| use zero-shot task split (disjoint classes in meta-train/val) instead of MAML split (ablation) | ZSML · AWA2 / CUB ZSL | accuracy: +1.7% / +2.4% | Helped |
| add meta-learner to discriminator (ablation) | ZSML · CUB / AWA2 ZSL | accuracy: 5.8% / 8.6% drop when removed | Helped |
| add meta-learner to generator (ablation) | ZSML · CUB / AWA2 ZSL | accuracy: 2.2% / 7.9% drop when removed | Helped |
| ZSML vs prior generative GZSL methods | GZSL · aPY | harmonic mean: 36.6 -> 40.9 | Helped |
| ZSML with 5 examples per seen class vs baselines | few-shot GZSL · AwA2 | harmonic mean: 41.8 -> 49.1 | Helped |
| ZSML vs prior state of the art | ZSL · SUN | per-class accuracy | No clear effect |
Closest papers by meaning
- Task Aligned Generative Meta-learning for Zero-shot Learning (AAAI 2021 · similarity 0.93)
- Gradient Matching Generative Networks for Zero-Shot Learning (CVPR 2019 · similarity 0.92)
- Data Distribution Distilled Generative Model for Generalized Zero-Shot Recognition (AAAI 2024 · similarity 0.91)
- Data-Free Generalized Zero-Shot Learning (AAAI 2024 · similarity 0.91)
- GenZSL: Generative Zero-Shot Learning Via Inductive Variational Autoencoder (ICML 2025 · similarity 0.90)
- Improving Generalized Zero-Shot Learning by Exploring the Diverse Semantics from External Class Names (CVPR 2024 · similarity 0.89)
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