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Deep metric learning using Triplet network
Hoffer, Elad...

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Deep metric learning using Triplet network by Hoffer, Elad ( Author )
Lemonjar Open Access
Australian National University
06-09-2023
Deep learning has proven itself as a successful set of models for learning useful semantic representations of data. These, however, are mostly implicitly learned as part of a classification task. In this paper we propose the triplet network model, which aims to learn useful representations by distance comparisons. A similar model was defined by Wang et al. (2014), tailor made for learning a ranking for image information retrieval. Here we demonstrate using various datasets that our model learns a better representation than that of its immediate competitor, the Siamese network. We also discuss future possible usage as a framework for unsupervised learning.
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Article
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English
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MYR 0.01
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http://arxiv.org/abs/1412.6622
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