%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 }