Deep Metric Learning Using Triplet Network
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.
Cite
Text
Hoffer and Ailon. "Deep Metric Learning Using Triplet Network." International Conference on Learning Representations, 2015. doi:10.1007/978-3-319-24261-3_7Markdown
[Hoffer and Ailon. "Deep Metric Learning Using Triplet Network." International Conference on Learning Representations, 2015.](https://mlanthology.org/iclr/2015/hoffer2015iclr-deep/) doi:10.1007/978-3-319-24261-3_7BibTeX
@inproceedings{hoffer2015iclr-deep,
title = {{Deep Metric Learning Using Triplet Network}},
author = {Hoffer, Elad and Ailon, Nir},
booktitle = {International Conference on Learning Representations},
year = {2015},
doi = {10.1007/978-3-319-24261-3_7},
url = {https://mlanthology.org/iclr/2015/hoffer2015iclr-deep/}
}