  Distance metric learning (DML) approaches learn a transformation to a
representation space where distance is in correspondence with a predefined
notion of similarity. While such models offer a number of compelling benefits,
it has been difficult for these to compete with modern classification
algorithms in performance and even in feature extraction.
  In this work, we propose a novel approach explicitly designed to address a
number of subtle yet important issues which have stymied earlier DML
algorithms. It maintains an explicit model of the distributions of the
different classes in representation space. It then employs this knowledge to
adaptively assess similarity, and achieve local discrimination by penalizing
class distribution overlap.
  We demonstrate the effectiveness of this idea on several tasks. Our approach
achieves state-of-the-art classification results on a number of fine-grained
visual recognition datasets, surpassing the standard softmax classifier and
outperforming triplet loss by a relative margin of 30-40%. In terms of
computational performance, it alleviates training inefficiencies in the
traditional triplet loss, reaching the same error in 5-30 times fewer
iterations. Beyond classification, we further validate the saliency of the
learnt representations via their attribute concentration and hierarchy recovery
properties, achieving 10-25% relative gains on the softmax classifier and
25-50% on triplet loss in these tasks.
