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.
%0 Generic
%1 hoffer2014metric
%A Hoffer, Elad
%A Ailon, Nir
%D 2014
%K loss triplet
%T Deep metric learning using Triplet network
%U http://arxiv.org/abs/1412.6622
%X 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.
@misc{hoffer2014metric,
abstract = {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.},
added-at = {2021-05-10T09:12:24.000+0200},
author = {Hoffer, Elad and Ailon, Nir},
biburl = {https://www.bibsonomy.org/bibtex/26fe106798b1007d1988aa24e969e0ef1/parismic},
description = {Deep metric learning using Triplet network},
interhash = {039e4847b1f9e26d66d62d604fc4c035},
intrahash = {6fe106798b1007d1988aa24e969e0ef1},
keywords = {loss triplet},
note = {cite arxiv:1412.6622},
timestamp = {2021-05-10T09:12:24.000+0200},
title = {Deep metric learning using Triplet network},
url = {http://arxiv.org/abs/1412.6622},
year = 2014
}