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Cited by 703Face and Expression RecognitionDomain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Deep Metric Learning: A Survey

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KAYA, Mahmut, BİLGE, Hasan Şakir·2019·Symmetry
DOI 10.3390/sym11091066
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Abstract

Metric 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.

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Cited by

Sorted by citations

Authors
KAYA, M., & BİLGE, H. Ş.
Affiliations
  • Siirt Üniversitesi (TR)
  • Department of Electrical - Electronics Engineering, Engineering Faculty, Gazi University, Ankara 06570, Turkey
Corresponding author
KAYA, Mahmut
ORCID
KAYA, Mahmut
Venue
Symmetry
Publisher
MDPI AG
Published
2019-08-21
Volume
11
Issue
9
Pages
1066
ISSN
2073-8994 (ISSN-L)
DOI
10.3390/sym11091066
OpenAlex
W2969656782
Cited by
703
References
6
Citations by year
2026: 68 · 2025: 105 · 2024: 152 · 2023: 146 · 2022: 112 · 2021: 83 · 2020: 35 · 2019: 2
FWCI
25.92
Type
journal article
Language
en
Open access
Licence
Locations
  • Symmetry (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
deep metric learning, similarity learning, Siamese network, triplet network, parameter sharing, metric learning, kernel methods, sampling strategy, nonlinear data, activation functions

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