Learning Embeddings For Product Visual Search With Triplet Loss And Online Sampling
Eric Dodds, Huy Nguyen, Simao Herdade, Jack Culpepper, Andrew Kae, Pierre Garrigues . Arxiv 2018 – 1 citation
[Paper]
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.