So sánh phương pháp

	Học chuyển giao bán giám sát	Học tăng cường tự giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2010s	2018–2020
Người khởi xướng	Pan, S. J. & Yang, Q. (formalized); wider community	LeCun, Y. and community (formalized ~2018–2020)
Loại	Hybrid learning paradigm	Representation learning paradigm
Công trình gốc	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.	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/
Tên gọi khác	SSTL, semi-supervised domain adaptation, transfer learning with unlabeled data, few-label transfer learning	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Liên quan	4	3
Tóm tắt	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.	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.