So sánh phương pháp

	Học tăng cường tự giám sát	Học bán giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2018–2020	1970s–2006 (formalized)
Người khởi xướng	LeCun, Y. and community (formalized ~2018–2020)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Loại	Representation learning paradigm	Learning paradigm
Công trình gốc	LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Tên gọi khác	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Liên quan	3	5
Tóm tắt	Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples.	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.