  In deep metric learning, the training procedure relies on sampling
informative tuples. However, as the training procedure progresses, it becomes
nearly impossible to sample relevant hard negative examples without proper
mining strategies or generation-based methods. Recent work on hard negative
generation have shown great promises to solve the mining problem. However, this
generation process is difficult to tune and often leads to incorrectly labelled
examples. To tackle this issue, we introduce MIRAGE, a generation-based method
that relies on virtual classes entirely composed of generated examples that act
as buffer areas between the training classes. We empirically show that virtual
classes significantly improve the results on popular datasets (Cub-200-2011,
Cars-196 and Stanford Online Products) compared to other generation methods.
