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Mësimi i Metrikës×Rrjeti Neural Siamese×
FushaMësimi i makinësMësimi i thellë
FamiljaMachine learningMachine learning
Viti i origjinës2003 (foundational); refined 2009 (LMNN)1993
KrijuesiXing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y.Jane Bromley & Yann LeCun et al.; popularized by Koch et al.
LlojiRepresentation learning / supervised distance optimizationDeep metric-learning architecture
Burimi themeluesXing, 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. link ↗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. link ↗
Emërtime të tjeraDistance Metric Learning, Similarity Learning, DML, Representation Learning via Distancetwin network, Siamese neural network, contrastive metric network, Siyam ağı
Të lidhura51
PërmbledhjaMetric 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.
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ScholarGateKrahasoni metodat: Metric Learning · Siamese Network. Marrë më 2026-06-17 nga https://scholargate.app/sq/compare