Сравнение методов

	Метрическое обучение	Обучение на малом числе примеров (Few-shot Learning)
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2003 (foundational); refined 2009 (LMNN)	2011–2017
Автор метода	Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y.	Lake, B. M.; Vinyals, O.; Finn, C. et al.
Тип	Representation learning / supervised distance optimization	Meta-learning / low-data learning paradigm
Основополагающий источник	Xing, E. P., Jordan, M. I., Russell, S., & Ng, A. Y. (2003). Distance metric learning with application to clustering with side-information. In Advances in Neural Information Processing Systems (NIPS), 16, 505–512.	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.
Другие названия	Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance	FSL, low-shot learning, k-shot learning, meta-learning for few examples
Связанные	5	4
Сводка	Metric learning is a machine-learning framework that trains a distance or similarity function from data so that semantically similar examples end up close together in the learned space while dissimilar examples are pushed apart. Unlike fixed distances such as Euclidean, the learned metric adapts to the structure of the task, making downstream classifiers, clusterers, and retrieval systems significantly more accurate.	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.