Methoden vergleichen

	Metrik-Lernen	Few-Shot Learning
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2003 (foundational); refined 2009 (LMNN)	2011–2017
Urheber	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
Wegweisende Quelle	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.
Aliasnamen	Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance	FSL, low-shot learning, k-shot learning, meta-learning for few examples
Verwandt	5	4
Zusammenfassung	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.