Papers with inference of unseen classes
CAARMA: Class Augmentation with Adversarial Mixup Regularization (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Speaker verification tasks require inference of unseen classes using specialized losses. |
| Approach: | They propose a class augmentation framework that generates synthetic classes through data mixing in the embedding space. |
| Outcome: | The proposed framework improves speaker verification tasks by 8% over baseline models. |