Research map / Label-efficient and robust learning / Zero-/few-shot and meta-learning
Generative Dual Adversarial Network for Generalized Zero-Shot Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add the Discriminator to GDAN (ablation) | GDAN · SUN, CUB, AwA2, aPY | unseen class accuracy: 37.3 -> 38.1; 38.2 -> 39.3; 31.4 -> 32.1; 29.6 -> 30.4 | Helped |
| Add the Regressor to GDAN (ablation) | GDAN · SUN, CUB, AwA2, aPY | unseen class accuracy: 37.4 -> 38.1; 38.1 -> 39.3; 30.9 -> 32.1; 29.3 -> 30.4 | Helped |
| Increase the number of synthetic examples | GDAN · CUB and SUN | unseen class accuracy | Helped |
| Increase the number of synthetic examples | GDAN · AwA2 | unseen class accuracy | No clear effect |
Closest papers by meaning
- Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning (NeurIPS 2019 · similarity 0.92)
- Feature Generating Networks for Zero-Shot Learning (CVPR 2018 · similarity 0.92)
- Adaptive and Generative Zero-Shot Learning (ICLR 2021 · similarity 0.92)
- Leveraging the Invariant Side of Generative Zero-Shot Learning (CVPR 2019 · similarity 0.91)
- Improving Generalized Zero-Shot Learning by Exploring the Diverse Semantics from External Class Names (CVPR 2024 · similarity 0.88)
- Visual and Semantic Prompt Collaboration for Generalized Zero-Shot Learning (CVPR 2025 · similarity 0.87)
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