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
this https URL

本文通过对深度度量学习算法的客观比较研究，发现了深度度量学习算法饱和度高于文献中表明的水平，并揭示了嵌入空间密度与模型泛化性能之间的相关性，并提出了一种简单有效的训练正则化方法来提高基于排名的深度度量学习模型的性能。