So sánh phương pháp

	Học chuyển giao Bayes	Học chuyển giao bán giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2006–2010	2010s
Người khởi xướng	Raina, R.; Ng, A. Y.; Koller, D. (and subsequent community)	Pan, S. J. & Yang, Q. (formalized); wider community
Loại	Probabilistic transfer / domain adaptation framework	Hybrid learning paradigm
Công trình gốc	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.	Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., & He, Q. (2021). A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1), 43–76.
Tên gọi khác	BTL, Bayesian domain adaptation, probabilistic transfer learning, Bayesian knowledge transfer	SSTL, semi-supervised domain adaptation, transfer learning with unlabeled data, few-label transfer learning
Liên quan	4	4
Tóm tắt	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.	Semi-supervised Transfer Learning combines knowledge transferred from a richly labeled source domain with the structure of abundant unlabeled target-domain data, using only a small set of labeled target examples to achieve strong generalization where full annotation is scarce or expensive.