A Siamese neural network (sometimes called a twin neural network) is an artificial neural network that uses the same weights while working in tandem on two different input vectors to compute comparable output vectors.[1][2][3][4] Often one of the output vectors is precomputed, thus forming a baseline against which the other output vector is compared. This is similar to comparing fingerprints but can be described more technically as a distance function for locality-sensitive hashing.[citation needed]
It is possible to build an architecture that is functionally similar to a twin network but implements a slightly different function. This is typically used for comparing similar instances in different type sets.[citation needed]
Uses of similarity measures where a twin network might be used are such things as recognizing handwritten checks, automatic detection of faces in camera images, and matching queries with indexed documents. The perhaps most well-known application of twin networks are face recognition, where known images of people are precomputed and compared to an image from a turnstile or similar. It is not obvious at first, but there are two slightly different problems. One is recognizing a person among a large number of other persons, that is the facial recognition problem. DeepFace is an example of such a system.[4] In its most extreme form this is recognizing a single person at a train station or airport. The other is face verification, that is to verify whether the photo in a pass is the same as the person claiming he or she is the same person. The twin network might be the same, but the implementation can be quite different.
^Chicco, Davide (2020), "Siamese neural networks: an overview", Artificial Neural Networks, Methods in Molecular Biology, vol. 2190 (3rd ed.), New York City, New York, USA: Springer Protocols, Humana Press, pp. 73–94, doi:10.1007/978-1-0716-0826-5_3, ISBN 978-1-0716-0826-5, PMID 32804361, S2CID 221144012
^Bromley, Jane; Guyon, Isabelle; LeCun, Yann; Säckinger, Eduard; Shah, Roopak (1994). "Signature verification using a "Siamese" time delay neural network" (PDF). Advances in Neural Information Processing Systems. 6: 737–744.
^Chopra, S.; Hadsell, R.; LeCun, Y. (June 2005). "Learning a Similarity Metric Discriminatively, with Application to Face Verification". 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). Vol. 1. pp. 539–546 vol. 1. doi:10.1109/CVPR.2005.202. ISBN 0-7695-2372-2. S2CID 5555257.
^ abTaigman, Y.; Yang, M.; Ranzato, M.; Wolf, L. (June 2014). "DeepFace: Closing the Gap to Human-Level Performance in Face Verification". 2014 IEEE Conference on Computer Vision and Pattern Recognition. pp. 1701–1708. doi:10.1109/CVPR.2014.220. ISBN 978-1-4799-5118-5. S2CID 2814088.
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