手法を比較

	半教師あり転移学習	転移学習
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	2010s	2010 (formalized); 1990s (early roots)
提唱者	Pan, S. J. & Yang, Q. (formalized); wider community	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
種類	Hybrid learning paradigm	Learning paradigm
原典	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.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
別名	SSTL, semi-supervised domain adaptation, transfer learning with unlabeled data, few-label transfer learning	TL, domain adaptation, fine-tuning, pre-trained model adaptation
関連	4	3
概要	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.	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.