Comparer des méthodes

	Apprentissage par ensemble à faible nombre d'exemples	Boosting
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2019	1990–1997
Auteur d'origine	Dvornik, N., Schmid, C., & Mairal, J.	Schapire, R. E.; Freund, Y.
Type	Ensemble of few-shot learners	Sequential ensemble (iterative reweighting)
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.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.
Alias	ensemble few-shot classification, multi-model few-shot learning, few-shot ensemble, cooperative few-shot ensemble	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Apparentées	5	6
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.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.