手法を比較

	ベイジアン転移学習	半教師あり転移学習
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	2006–2010	2010s
提唱者	Raina, R.; Ng, A. Y.; Koller, D. (and subsequent community)	Pan, S. J. & Yang, Q. (formalized); wider community
種類	Probabilistic transfer / domain adaptation framework	Hybrid learning paradigm
原典	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.
別名	BTL, Bayesian domain adaptation, probabilistic transfer learning, Bayesian knowledge transfer	SSTL, semi-supervised domain adaptation, transfer learning with unlabeled data, few-label transfer learning
関連	4	4
概要	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.