Deep Metric Learning: A Survey

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dc.authoridKAYA, Mahmut/0000-0002-7846-1769
dc.contributor.authorKaya, Mahmut
dc.contributor.authorBilge, Hasan Sakir
dc.date.accessioned2024-12-24T19:33:45Z
dc.date.available2024-12-24T19:33:45Z
dc.date.issued2019
dc.departmentSiirt Üniversitesi
dc.description.abstractMetric learning aims to measure the similarity among samples while using an optimal distance metric for learning tasks. Metric learning methods, which generally use a linear projection, are limited in solving real-world problems demonstrating non-linear characteristics. Kernel approaches are utilized in metric learning to address this problem. In recent years, deep metric learning, which provides a better solution for nonlinear data through activation functions, has attracted researchers' attention in many different areas. This article aims to reveal the importance of deep metric learning and the problems dealt with in this field in the light of recent studies. As far as the research conducted in this field are concerned, most existing studies that are inspired by Siamese and Triplet networks are commonly used to correlate among samples while using shared weights in deep metric learning. The success of these networks is based on their capacity to understand the similarity relationship among samples. Moreover, sampling strategy, appropriate distance metric, and the structure of the network are the challenging factors for researchers to improve the performance of the network model. This article is considered to be important, as it is the first comprehensive study in which these factors are systematically analyzed and evaluated as a whole and supported by comparing the quantitative results of the methods.
dc.identifier.doi10.3390/sym11091066
dc.identifier.issn2073-8994
dc.identifier.issue9
dc.identifier.scopus2-s2.0-85071924035
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/sym11091066
dc.identifier.urihttps://hdl.handle.net/20.500.12604/8275
dc.identifier.volume11
dc.identifier.wosWOS:000489177900002
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofSymmetry-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectmetric learning
dc.subjectdeep metric learning
dc.subjectsimilarity
dc.subjectsiamese network
dc.subjecttriplet network
dc.titleDeep Metric Learning: A Survey
dc.typeReview Article

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