Deep Metric Learning Using Triplet Network. Hoffer, E. & Ailon, N. Paper abstract bibtex 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.
@article{hofferDeepMetricLearning2014,
archivePrefix = {arXiv},
eprinttype = {arxiv},
eprint = {1412.6622},
primaryClass = {cs, stat},
title = {Deep Metric Learning Using {{Triplet}} Network},
url = {http://arxiv.org/abs/1412.6622},
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.},
urldate = {2019-01-05},
date = {2014-12-20},
keywords = {Computer Science - Computer Vision and Pattern Recognition,Statistics - Machine Learning,Computer Science - Machine Learning},
author = {Hoffer, Elad and Ailon, Nir},
file = {/home/dimitri/Nextcloud/Zotero/storage/Z66BDQLS/Hoffer and Ailon - 2014 - Deep metric learning using Triplet network.pdf;/home/dimitri/Nextcloud/Zotero/storage/NRZ8EIN8/1412.html}
}
Downloads: 0
{"_id":"Ko8zDJQxfDxJvG8zJ","bibbaseid":"hoffer-ailon-deepmetriclearningusingtripletnetwork","authorIDs":[],"author_short":["Hoffer, E.","Ailon, N."],"bibdata":{"bibtype":"article","type":"article","archiveprefix":"arXiv","eprinttype":"arxiv","eprint":"1412.6622","primaryclass":"cs, stat","title":"Deep Metric Learning Using Triplet Network","url":"http://arxiv.org/abs/1412.6622","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.","urldate":"2019-01-05","date":"2014-12-20","keywords":"Computer Science - Computer Vision and Pattern Recognition,Statistics - Machine Learning,Computer Science - Machine Learning","author":[{"propositions":[],"lastnames":["Hoffer"],"firstnames":["Elad"],"suffixes":[]},{"propositions":[],"lastnames":["Ailon"],"firstnames":["Nir"],"suffixes":[]}],"file":"/home/dimitri/Nextcloud/Zotero/storage/Z66BDQLS/Hoffer and Ailon - 2014 - Deep metric learning using Triplet network.pdf;/home/dimitri/Nextcloud/Zotero/storage/NRZ8EIN8/1412.html","bibtex":"@article{hofferDeepMetricLearning2014,\n archivePrefix = {arXiv},\n eprinttype = {arxiv},\n eprint = {1412.6622},\n primaryClass = {cs, stat},\n title = {Deep Metric Learning Using {{Triplet}} Network},\n url = {http://arxiv.org/abs/1412.6622},\n 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.},\n urldate = {2019-01-05},\n date = {2014-12-20},\n keywords = {Computer Science - Computer Vision and Pattern Recognition,Statistics - Machine Learning,Computer Science - Machine Learning},\n author = {Hoffer, Elad and Ailon, Nir},\n file = {/home/dimitri/Nextcloud/Zotero/storage/Z66BDQLS/Hoffer and Ailon - 2014 - Deep metric learning using Triplet network.pdf;/home/dimitri/Nextcloud/Zotero/storage/NRZ8EIN8/1412.html}\n}\n\n","author_short":["Hoffer, E.","Ailon, N."],"key":"hofferDeepMetricLearning2014","id":"hofferDeepMetricLearning2014","bibbaseid":"hoffer-ailon-deepmetriclearningusingtripletnetwork","role":"author","urls":{"Paper":"http://arxiv.org/abs/1412.6622"},"keyword":["Computer Science - Computer Vision and Pattern Recognition","Statistics - Machine Learning","Computer Science - Machine Learning"],"downloads":0},"bibtype":"article","biburl":"https://raw.githubusercontent.com/dlozeve/newblog/master/bib/all.bib","creationDate":"2020-01-08T20:39:39.219Z","downloads":0,"keywords":["computer science - computer vision and pattern recognition","statistics - machine learning","computer science - machine learning"],"search_terms":["deep","metric","learning","using","triplet","network","hoffer","ailon"],"title":"Deep Metric Learning Using Triplet Network","year":null,"dataSources":["3XqdvqRE7zuX4cm8m"]}