방법 비교

	메트릭 학습	Siamese 신경망
분야	머신러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2003 (foundational); refined 2009 (LMNN)	1993
창시자	Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y.	Jane Bromley & Yann LeCun et al.; popularized by Koch et al.
유형	Representation learning / supervised distance optimization	Deep metric-learning architecture
원전	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.	Bromley, J., Guyon, I., LeCun, Y., Säckinger, E., & Shah, R. (1993). Signature verification using a 'Siamese' time delay neural network. Advances in Neural Information Processing Systems, 6.
별칭	Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance	twin network, Siamese neural network, contrastive metric network, Siyam ağı
관련	5	1
요약	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.	A Siamese network is a deep architecture with two (or more) identical, weight-sharing branches that map inputs into an embedding space where similar inputs land close together and dissimilar ones far apart. Introduced by Bromley, LeCun, and colleagues in 1993 for signature verification and revived by Koch et al. (2015) for one-shot image recognition, it learns a similarity metric rather than fixed class labels, making it ideal for verification, matching, and few-shot tasks.