So sánh phương pháp

	Học chuyển giao có điều chuẩn	Học chuyển giao bán giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2000s–2010s	2010s
Người khởi xướng	Pan, S. J. & Yang, Q. (survey); regularization variants by multiple authors	Pan, S. J. & Yang, Q. (formalized); wider community
Loại	Regularized supervised/semi-supervised learning framework	Hybrid learning paradigm
Công trình gốc	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.
Tên gọi khác	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
Liên quan	6	4
Tóm tắt	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.