  Deep metric learning aims to learn a function mapping image pixels to
embedding feature vectors that model the similarity between images. Two major
applications of metric learning are content-based image retrieval and face
verification. For the retrieval tasks, the majority of current state-of-the-art
(SOTA) approaches are triplet-based non-parametric training. For the face
verification tasks, however, recent SOTA approaches have adopted
classification-based parametric training. In this paper, we look into the
effectiveness of classification based approaches on image retrieval datasets.
We evaluate on several standard retrieval datasets such as CAR-196,
CUB-200-2011, Stanford Online Product, and In-Shop datasets for image retrieval
and clustering, and establish that our classification-based approach is
competitive across different feature dimensions and base feature networks. We
further provide insights into the performance effects of subsampling classes
for scalable classification-based training, and the effects of binarization,
enabling efficient storage and computation for practical applications.
