Research map / Label-efficient and robust learning / Zero-/few-shot and meta-learning
Zero-Shot Recognition via Direct Classifier Learning with Transferred Samples and Pseudo Labels
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use one-step direct classifiers with pseudo labels instead of two-step attribute-space ZSR (inductive) | Zero-shot recognition · CIFAR10 | accuracy (%): 88.30 -> 89.52 | Helped |
| Inductive proposed vs best two-step baseline on aPY | Ours-Inductive · aPascal-aYahoo | accuracy (%): 46.23 -> 43.59 | Hurt |
| Add unlabeled target samples with iterative label refinement (transductive) (ablation) | Ours · aPY | accuracy (%): 43.59 -> 50.84 | Helped |
| Add unlabeled target samples with iterative refinement (ablation) | Ours · AwA | accuracy (%): 79.07 -> 81.90 | Helped |
| Increase number of selected source samples ms (ablation) | Ours · CIFAR10 | accuracy | Helped |
| Increase ms beyond 400 to 600 (ablation) | Ours · aPY and SUN | accuracy | Hurt |
| Increase noise parameter mu of robust SVM (ablation) | Ours · four datasets | accuracy | Mixed |
Closest papers by meaning
- Transductive Zero-Shot Recognition via Shared Model Space Learning (AAAI 2016 · similarity 0.92)
- Zero-Shot Learning via Joint Latent Similarity Embedding (CVPR 2016 · similarity 0.91)
- Dual-View Ranking with Hardness Assessment for Zero-Shot Learning (AAAI 2019 · similarity 0.91)
- Zero-Shot Learning With Attribute Selection (AAAI 2018 · similarity 0.90)
- Make an Omelette with Breaking Eggs: Zero-Shot Learning for Novel Attribute Synthesis (NeurIPS 2022 · similarity 0.90)
- Data-Free Generalized Zero-Shot Learning (AAAI 2024 · similarity 0.88)
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