Comparer des méthodes

	Apprentissage actif en ligne	Apprentissage à peu d'exemples
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2000s	2011–2017
Auteur d'origine	Cesa-Bianchi, N. and others (multiple contributors)	Lake, B. M.; Vinyals, O.; Finn, C. et al.
Type	Hybrid learning paradigm (online + active)	Meta-learning / low-data learning paradigm
Source fondatrice	Cesa-Bianchi, N., Gentile, C., & Zaniboni, L. (2006). Worst-case analysis of selective sampling for linear classification. Journal of Machine Learning Research, 7, 1205–1230.	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	streaming active learning, online query-by-committee, sequential active learning, incremental active learning	FSL, low-shot learning, k-shot learning, meta-learning for few examples
Apparentées	6	4
Résumé	Online active learning combines two complementary paradigms: it processes data as a stream (online learning) and selectively requests labels only for the most informative instances (active learning). The result is a model that adapts continuously to new data while keeping labeling costs low — useful whenever labeled data is expensive and examples arrive sequentially rather than all at once.	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.