Сравнение методов

	Ансамблевое обучение с малым количеством примеров	Перенос обучения
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2019	2010 (formalized); 1990s (early roots)
Автор метода	Dvornik, N., Schmid, C., & Mairal, J.	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Тип	Ensemble of few-shot learners	Learning paradigm
Основополагающий источник	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.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Другие названия	ensemble few-shot classification, multi-model few-shot learning, few-shot ensemble, cooperative few-shot ensemble	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Связанные	5	3
Сводка	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.	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.