  Metric learning aims to construct an embedding where two extracted features
corresponding to the same identity are likely to be closer than features from
different identities. This paper presents a method for learning such a feature
space where the cosine similarity is effectively optimized through a simple
re-parametrization of the conventional softmax classification regime. At test
time, the final classification layer can be stripped from the network to
facilitate nearest neighbor queries on unseen individuals using the cosine
similarity metric. This approach presents a simple alternative to direct metric
learning objectives such as siamese networks that have required sophisticated
pair or triplet sampling strategies in the past. The method is evaluated on two
large-scale pedestrian re-identification datasets where competitive results are
achieved overall. In particular, we achieve better generalization on the test
set compared to a network trained with triplet loss.
