Comparer des méthodes

	Apprentissage par transfert en ligne	Apprentissage à peu d'exemples
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2010	2011–2017
Auteur d'origine	Zhao, P. & Hoi, S. C. H.	Lake, B. M.; Vinyals, O.; Finn, C. et al.
Type	Online learning with source-domain knowledge transfer	Meta-learning / low-data learning paradigm
Source fondatrice	Zhao, P., & Hoi, S. C. H. (2010). OTL: A Framework of Online Transfer Learning. In Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 1231–1238. Omnipress.	Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., & Kavukcuoglu, K. (2016). Matching Networks for One Shot Learning. Advances in Neural Information Processing Systems (NeurIPS), 29.
Alias	OTL, streaming transfer learning, incremental transfer learning, online domain adaptation	FSL, low-shot learning, k-shot learning, meta-learning for few examples
Apparentées	4	4
Résumé	Online Transfer Learning (OTL) extends transfer learning to sequential, streaming settings: instead of training on a fixed dataset, the model processes examples one at a time and simultaneously leverages knowledge from a related source domain to improve predictions on the target domain without requiring large labeled target datasets upfront.	Few-shot learning is a machine learning paradigm that trains models to recognize new classes or solve new tasks from only a handful of labeled examples — typically one to five — by leveraging prior knowledge acquired from a large, related training distribution. It is especially relevant in domains where labeling is expensive, scarce, or structurally limited.