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• W3011837252 abstract "This article aims to demonstrate the value of deep metric learning considering recent studies. Most existing studies are inspired by Siamese and Triplet networks that build a high-level representation of samples in separable linear space. The success and main idea of these networks is the ability to capture the similarity relationship between samples in the dataset. There are mainly three components: sampling method, loss function, and neural network architecture that determine the metric learning model. Improvement of these components is the main challenge for researchers. This article is a review and analysis of deep metric learning components." @default.
• W3011837252 created "2020-03-23" @default.
• W3011837252 creator A5003551354 @default.
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• W3011837252 date "2019-12-01" @default.
• W3011837252 modified "2023-09-26" @default.
• W3011837252 title "Analysis of Deep Metric Learning Approaches" @default.
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• W3011837252 doi "https://doi.org/10.1109/atit49449.2019.9030440" @default.
• W3011837252 hasPublicationYear "2019" @default.
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