  In this paper, we propose learning an embedding function for content-based
image retrieval within the e-commerce domain using the triplet loss and an
online sampling method that constructs triplets from within a minibatch. We
compare our method to several strong baselines as well as recent works on the
DeepFashion and Stanford Online Product datasets. Our approach significantly
outperforms the state-of-the-art on the DeepFashion dataset. With a
modification to favor sampling minibatches from a single product category, the
same approach demonstrates competitive results when compared to the
state-of-the-art for the Stanford Online Products dataset.
