  Deep metric learning papers from the past four years have consistently
claimed great advances in accuracy, often more than doubling the performance of
decade-old methods. In this paper, we take a closer look at the field to see if
this is actually true. We find flaws in the experimental methodology of
numerous metric learning papers, and show that the actual improvements over
time have been marginal at best.
