So sánh phương pháp

	Học tăng cường bán giám sát với ít mẫu (Semi-supervised Few-shot Learning)	Học bán giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2018	1970s–2006 (formalized)
Người khởi xướng	Ren, M. et al. (ICLR 2018); builds on Finn et al. (MAML, 2017)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Loại	Meta-learning with unlabeled auxiliary data	Learning paradigm
Công trình gốc	Ren, M., Triantafillou, E., Ravi, S., Snell, J., Swersky, K., Tenenbaum, J. B., Larochelle, H., & Zemel, R. S. (2018). Meta-learning for semi-supervised few-shot classification. In International Conference on Learning Representations (ICLR 2018).	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Tên gọi khác	SS-FSL, semi-supervised meta-learning, few-shot learning with unlabeled data, low-label few-shot learning	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Liên quan	4	5
Tóm tắt	Semi-supervised Few-shot Learning (SS-FSL) trains models to classify new classes from only a handful of labeled examples per class, while simultaneously leveraging a pool of unlabeled data to enrich class representations. By combining meta-learning episodes with soft pseudo-label assignment for unlabeled samples, it achieves notably higher accuracy than purely supervised few-shot methods when abundant unlabeled data is available.	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.