Сравнение методов

	Байесовский перенос знаний	Перенос обучения
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2006–2010	2010 (formalized); 1990s (early roots)
Автор метода	Raina, R.; Ng, A. Y.; Koller, D. (and subsequent community)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Тип	Probabilistic transfer / domain adaptation framework	Learning paradigm
Основополагающий источник	Raina, R., Ng, A. Y., & Koller, D. (2006). Constructing informative priors using transfer learning. In Proceedings of the 23rd International Conference on Machine Learning (ICML), pp. 713–720. ACM.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Другие названия	BTL, Bayesian domain adaptation, probabilistic transfer learning, Bayesian knowledge transfer	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Связанные	4	3
Сводка	Bayesian Transfer Learning is a probabilistic framework that uses knowledge from a data-rich source domain to construct informative priors for a model trained on a data-scarce target domain. By encoding source-domain knowledge as prior distributions over parameters, the framework lets the model generalize well on the target task even with very limited labeled examples.	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.