Сравнение методов

	Онлайн-обучение с переносом знаний (Online Transfer Learning, OTL)	Перенос обучения
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2010	2010 (formalized); 1990s (early roots)
Автор метода	Zhao, P. & Hoi, S. C. H.	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Тип	Online learning with source-domain knowledge transfer	Learning paradigm
Основополагающий источник	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.
Другие названия	OTL, streaming transfer learning, incremental transfer learning, online domain adaptation	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Связанные	4	3
Сводка	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.