Deep metric learning using Triplet network
This work addresses representation learning for tasks like image retrieval, but it is incremental as it builds on prior ranking models.
The paper tackles the problem of learning semantic representations through distance comparisons by proposing the triplet network model, demonstrating that it learns better representations than the Siamese network on various datasets.
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.