  Deep Metric Learning (DML) is arguably one of the most influential lines of
research for learning visual similarities with many proposed approaches every
year. Although the field benefits from the rapid progress, the divergence in
training protocols, architectures, and parameter choices make an unbiased
comparison difficult. To provide a consistent reference point, we revisit the
most widely used DML objective functions and conduct a study of the crucial
parameter choices as well as the commonly neglected mini-batch sampling
process. Under consistent comparison, DML objectives show much higher
saturation than indicated by literature. Further based on our analysis, we
uncover a correlation between the embedding space density and compression to
the generalization performance of DML models. Exploiting these insights, we
propose a simple, yet effective, training regularization to reliably boost the
performance of ranking-based DML models on various standard benchmark datasets.
Code and a publicly accessible WandB-repo are available at
https://github.com/Confusezius/Revisiting_Deep_Metric_Learning_PyTorch.
