Title: A Unified View of Deep Metric Learning via Gradient Analysis. Abstract: Loss functions play a pivotal role in deep metric learning (DML). A large variety of loss functions have been proposed in DML recently. However, it remains difficult to answer this question: what are the intrinsic differences among these loss functions?This paper answers this question by proposing a unified perspective to rethink deep metric loss functions. We show theoretically that most DML methods in deep metric learning, in view of  gradient equivalence, are essentially weight assignment strategies of training pairs. Based on this unified view, we revisit several typical DML methods and disclose their hidden drawbacks. Moreover, we point out the key components of an effective DML approach which drives us to propose our weight assignment framework. We evaluate our method on image retrieval tasks, and show that it outperforms  the state-of-the-art DML approaches by a significant margin on the CUB-200-2011, Cars-196, Stanford Online Products and In-Shop Clothes Retrieval datasets. 