  Deep Metric Learning (DML) plays an important role in modern computer vision
research, where we learn a distance metric for a set of image representations.
Recent DML techniques utilize the proxy to interact with the corresponding
image samples in the embedding space. However, existing proxy-based DML methods
focus on learning individual proxy-to-sample distance while the overall
distribution of samples and proxies lacks attention. In this paper, we present
a novel proxy-based DML framework that focuses on aligning the sample and proxy
distributions to improve the efficiency of proxy-based DML losses.
Specifically, we propose the Data-Augmented Domain Adaptation (DADA) method to
adapt the domain gap between the group of samples and proxies. To the best of
our knowledge, we are the first to leverage domain adaptation to boost the
performance of proxy-based DML. We show that our method can be easily plugged
into existing proxy-based DML losses. Our experiments on benchmarks, including
the popular CUB-200-2011, CARS196, Stanford Online Products, and In-Shop
Clothes Retrieval, show that our learning algorithm significantly improves the
existing proxy losses and achieves superior results compared to the existing
methods.
