Comparer des méthodes

	Apprentissage par transfert bayésien	Apprentissage par transfert semi-supervisé
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2006–2010	2010s
Auteur d'origine	Raina, R.; Ng, A. Y.; Koller, D. (and subsequent community)	Pan, S. J. & Yang, Q. (formalized); wider community
Type	Probabilistic transfer / domain adaptation framework	Hybrid learning paradigm
Source fondatrice	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.
Alias	BTL, Bayesian domain adaptation, probabilistic transfer learning, Bayesian knowledge transfer	SSTL, semi-supervised domain adaptation, transfer learning with unlabeled data, few-label transfer learning
Apparentées	4	4
Résumé	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.