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	Semi-supervised Few-shot Learning	Uczenie transferowe
Dziedzina	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	2018	2010 (formalized); 1990s (early roots)
Twórca	Ren, M. et al. (ICLR 2018); builds on Finn et al. (MAML, 2017)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Typ	Meta-learning with unlabeled auxiliary data	Learning paradigm
Źródło pierwotne	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).	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Inne nazwy	SS-FSL, semi-supervised meta-learning, few-shot learning with unlabeled data, low-label few-shot learning	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Pokrewne	4	3
Podsumowanie	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.	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.