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	Uczenie metryczne	Uczenie z niewielką liczbą przykładów
Dziedzina	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	2003 (foundational); refined 2009 (LMNN)	2011–2017
Twórca	Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y.	Lake, B. M.; Vinyals, O.; Finn, C. et al.
Typ	Representation learning / supervised distance optimization	Meta-learning / low-data learning paradigm
Źródło pierwotne	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.
Inne nazwy	Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance	FSL, low-shot learning, k-shot learning, meta-learning for few examples
Pokrewne	5	4
Podsumowanie	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.