Papers with source-free domain adaptation
Source-Free Unsupervised Domain Adaptation for Question Answering via Prompt-Assisted Self-learning (2024.findings-naacl)
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| Challenge: | Existing SFDA methods focus on the adaptation phase, overlooking the impact of source domain training on model generalizability. |
| Approach: | They propose a source-free domain adaptation approach for Question Answering where a model trained on a domain is adapted to unlabeled target domains without additional source data. |
| Outcome: | The proposed model outperforms existing methods in managing domain gaps and demonstrating greater stability across target domains. |
A Comparison of Strategies for Source-Free Domain Adaptation (2022.acl-long)
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| Challenge: | Existing research on domain adaptation without access to training data is limited due to privacy concerns. |
| Approach: | They compare active learning, self-training, and data augmentation strategies for source-free domain adaptation with a shared task. |
| Outcome: | The proposed algorithms yield consistent gains across all SemEval 2021 Task 10 tasks and domains, but they are unreliable for source-free domain adaptation. |