Σύγκριση μεθόδων

	Εκμάθηση Μετρικής	Εκμάθηση με λίγα δείγματα
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2003 (foundational); refined 2009 (LMNN)	2011–2017
Δημιουργός	Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y.	Lake, B. M.; Vinyals, O.; Finn, C. et al.
Τύπος	Representation learning / supervised distance optimization	Meta-learning / low-data learning paradigm
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance	FSL, low-shot learning, k-shot learning, meta-learning for few examples
Συναφείς	5	4
Σύνοψη	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.