Comparer des méthodes

	Apprentissage par ensemble à faible nombre d'exemples	Apprentissage à peu d'exemples
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2019	2011–2017
Auteur d'origine	Dvornik, N., Schmid, C., & Mairal, J.	Lake, B. M.; Vinyals, O.; Finn, C. et al.
Type	Ensemble of few-shot learners	Meta-learning / low-data learning paradigm
Source fondatrice	Dvornik, N., Schmid, C., & Mairal, J. (2019). Diversity with Cooperation: Ensemble Methods for Few-Shot Classification. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 3716–3725.	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	ensemble few-shot classification, multi-model few-shot learning, few-shot ensemble, cooperative few-shot ensemble	FSL, low-shot learning, k-shot learning, meta-learning for few examples
Apparentées	5	4
Résumé	Ensemble Few-Shot Learning combines multiple few-shot models — such as prototypical networks or embedding learners — to classify new classes from only one to a handful of labeled examples. By enforcing diversity among base learners and aggregating their predictions, the ensemble consistently outperforms any single few-shot model in accuracy and robustness, especially under severe label scarcity.	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.