Porovnat metody

	Regularized Transfer Learning	Semisupervisední přenosové učení
Obor	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning
Rok vzniku	2000s–2010s	2010s
Tvůrce	Pan, S. J. & Yang, Q. (survey); regularization variants by multiple authors	Pan, S. J. & Yang, Q. (formalized); wider community
Typ	Regularized supervised/semi-supervised learning framework	Hybrid learning paradigm
Původní zdroj	Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.	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.
Další názvy	regularized domain adaptation, transfer learning with regularization, penalized transfer learning, regularized fine-tuning	SSTL, semi-supervised domain adaptation, transfer learning with unlabeled data, few-label transfer learning
Příbuzné	6	4
Shrnutí	Regularized Transfer Learning applies explicit penalty terms to a transfer learning pipeline to control how much a model shifts away from source-domain knowledge when adapting to a new target domain. The regularizer discourages negative transfer — the harmful carry-over of irrelevant source patterns — while preserving beneficial shared representations and preventing overfitting when target-domain labels are scarce.	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.