Research map / Label-efficient and robust learning / Zero-/few-shot and meta-learning
Zero Shot Learning with the Isoperimetric Loss
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add isoperimetric graph regularization to the linear ZSL baseline | Zero-IPL · AwA1 | top-1 accuracy (%): 58.6% to 76.03% | Helped |
| Add isoperimetric graph regularization to the linear ZSL baseline | Zero-IPL · AwA2 | top-1 accuracy (%): 58.6% to 73.46% | Helped |
| Add isoperimetric graph regularization to the linear ZSL baseline | Zero-IPL · CUB | top-1 accuracy (%): 23.8% to 39.45% | Helped |
| Change the k-nearest-neighbor graph parameter by 50% (ablation) | Zero-IPL · AwA1 | top-1 accuracy (%): performance drop of less than 2.6% | Hurt |
| Change the k-nearest-neighbor graph parameter by 50% (ablation) | Zero-IPL · AwA2 | top-1 accuracy (%): performance drop of less than 8.77% | Hurt |
| Change the k-nearest-neighbor graph parameter by 50% (ablation) | Zero-IPL · CUB | top-1 accuracy (%): performance drop of less than 2.02% | Hurt |
Closest papers by meaning
- Zero Shot Learning via Multi-Scale Manifold Regularization (CVPR 2017 · similarity 0.90)
- Matrix Tri-Factorization With Manifold Regularizations for Zero-Shot Learning (CVPR 2017 · similarity 0.88)
- Synthesized Classifiers for Zero-Shot Learning (CVPR 2016 · similarity 0.88)
- Adversarial Zero-shot Learning With Semantic Augmentation (AAAI 2018 · similarity 0.87)
- Isometric Propagation Network for Generalized Zero-shot Learning (ICLR 2021 · similarity 0.87)
- TPR: Topology-Preserving Reservoirs for Generalized Zero-Shot Learning (NeurIPS 2024 · similarity 0.87)
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