방법 비교

	온라인 메트릭 학습	Siamese 신경망
분야	머신러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2004–2009	1993
창시자	Shalev-Shwartz, S.; Singer, Y.; and others	Jane Bromley & Yann LeCun et al.; popularized by Koch et al.
유형	Online / incremental learning of distance metrics	Deep metric-learning architecture
원전	Shalev-Shwartz, S., Singer, Y., & Ng, A. Y. (2004). Online and batch learning of pseudo-metrics. Proceedings of the 21st International Conference on Machine Learning (ICML 2004), pp. 94. ACM.	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	OML, incremental metric learning, streaming metric learning, online distance metric learning	twin network, Siamese neural network, contrastive metric network, Siyam ağı
관련	3	1
요약	Online Metric Learning adapts a Mahalanobis distance metric incrementally as new labeled examples or pairwise constraints arrive one at a time, without storing the full dataset. It merges the efficiency of online learning with the representational power of metric learning, making it suitable for streaming, large-scale, or continually changing environments where retraining from scratch is impractical.	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.