방법 비교

	온라인 전이 학습	전이 학습
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2010	2010 (formalized); 1990s (early roots)
창시자	Zhao, P. & Hoi, S. C. H.	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
유형	Online learning with source-domain knowledge transfer	Learning paradigm
원전	Zhao, P., & Hoi, S. C. H. (2010). OTL: A Framework of Online Transfer Learning. In Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 1231–1238. Omnipress.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
별칭	OTL, streaming transfer learning, incremental transfer learning, online domain adaptation	TL, domain adaptation, fine-tuning, pre-trained model adaptation
관련	4	3
요약	Online Transfer Learning (OTL) extends transfer learning to sequential, streaming settings: instead of training on a fixed dataset, the model processes examples one at a time and simultaneously leverages knowledge from a related source domain to improve predictions on the target domain without requiring large labeled target datasets upfront.	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.