Comparer des méthodes

	Semi-supervised Few-shot Learning	Apprentissage par transfert
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2018	2010 (formalized); 1990s (early roots)
Auteur d'origine	Ren, M. et al. (ICLR 2018); builds on Finn et al. (MAML, 2017)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Type	Meta-learning with unlabeled auxiliary data	Learning paradigm
Source fondatrice	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.
Alias	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
Apparentées	4	3
Résumé	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.