# Deep metric learning using Triplet network

arXiv: https://arxiv.org/abs/1412.6622 · HTML: https://mlsanity.com/paper/1412.6622/

## Key facts

- Cited by 422 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2014.

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## Abstract (arXiv)

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.