Papers with Regularizer
Domain Adversarial Fine-Tuning as an Effective Regularizer (2020.findings-emnlp)
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| Challenge: | Existing fine-tuning techniques can degrade general-domain representations . however, fine-timing can lead to catastrophic forgetting of knowledge . |
| Approach: | They propose a new regularization technique that complements the task-specific loss used during fine-tuning with an adversarial objective. |
| Outcome: | Empirical results show that AFTER improves performance on various natural language understanding tasks compared to standard fine-tuning. |