Research map / Label-efficient and robust learning / Recognition and long-tail
Generative-Discriminative Feature Representations for Open-Set Recognition
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add self-supervision (ablation) | open-set classifier · CIFAR10 animal-known split | AUC-ROC: 84.4 -> 88.8 | Helped |
| augment the input with a generative representation, without self-supervision (ablation) | open-set classifier · CIFAR10 animal-known split | AUC-ROC: 84.4 -> 88.4 | Helped |
| combine generative feature augmentation and self-supervision (ablation) | open-set classifier · CIFAR10 animal-known split | AUC-ROC: 84.4 -> 91.2 | Helped |
| replace Vanilla AE with Conditioned-AE (ablation) | generative representation model · CIFAR10 | AUC-ROC: 80.8 -> 82.0 | Helped |
| use WRN28-10 instead of Vanilla CNN (ablation) | classifier with Conditioned-AE · CIFAR10 | AUC-ROC: 82.0 -> 83.1 | Helped |
| use WGAN instead of Conditioned-AE (ablation) | WRN28-10 classifier · CIFAR10 | AUC-ROC: 83.1 -> 83.6 | Helped |
Closest papers by meaning
- From Coarse to Fine-Grained Open-Set Recognition (CVPR 2024 · similarity 0.88)
- All Beings Are Equal in Open Set Recognition (AAAI 2024 · similarity 0.88)
- Exploring Diverse Representations for Open Set Recognition (AAAI 2024 · similarity 0.87)
- Unlocking Better Closed-Set Alignment Based on Neural Collapse for Open-Set Recognition (AAAI 2025 · similarity 0.87)
- Frequency Shuffling and Enhancement for Open Set Recognition (AAAI 2024 · similarity 0.86)
- Intriguing Properties of Generative Classifiers (ICLR 2024 · similarity 0.86)
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