So sánh phương pháp

	Học bán giám sát	Transfer Learning
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1970s–2006 (formalized)	2010 (formalized); 1990s (early roots)
Người khởi xướng	Vapnik, V. N. and others (community of researchers, 1970s–2000s)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Loại	Learning paradigm	Learning paradigm
Công trình gốc	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Tên gọi khác	SSL, semi-supervised machine learning, transductive learning, label-efficient learning	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Liên quan	5	3
Tóm tắt	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.