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| 도메인 적응 트랜스포머× | 전이 학습× | |
|---|---|---|
| 분야≠ | 딥러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2019–2022 | 2010 (formalized); 1990s (early roots) |
| 창시자≠ | Various (Vaswani et al. 2017 for Transformers; domain adaptation extensions emerged 2019–2022) | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| 유형≠ | Pre-trained model fine-tuned with domain-shift adaptation | Learning paradigm |
| 원전≠ | Ni, J., Hernandez Abrego, G., Constant, N., Ma, J., Hall, K., Cer, D., & Yang, Y. (2021). Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models. Findings of ACL 2022. arXiv:2108.08877. link ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| 별칭 | DAT, domain-adaptive Transformer, domain adaptation with Transformers, transfer-learning Transformer | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| 관련≠ | 2 | 3 |
| 요약≠ | A Domain-Adaptive Transformer (DAT) is a Transformer-based model — such as BERT or ViT — extended with an explicit domain-alignment objective so that learned representations transfer well from a labeled source domain to a different, often unlabeled, target domain. The approach combines the powerful representation capacity of Transformers with domain adaptation techniques such as adversarial training or contrastive alignment to minimise domain shift. | Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond. |
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