  One of the main purposes of deep metric learning is to construct an embedding
space that has well-generalized embeddings on both seen (training) classes and
unseen (test) classes. Most existing works have tried to achieve this using
different types of metric objectives and hard sample mining strategies with
given training data. However, learning with only the training data can be
overfitted to the seen classes, leading to the lack of generalization
capability on unseen classes. To address this problem, we propose a simple
regularizer called Proxy Synthesis that exploits synthetic classes for stronger
generalization in deep metric learning. The proposed method generates synthetic
embeddings and proxies that work as synthetic classes, and they mimic unseen
classes when computing proxy-based losses. Proxy Synthesis derives an embedding
space considering class relations and smooth decision boundaries for robustness
on unseen classes. Our method is applicable to any proxy-based losses,
including softmax and its variants. Extensive experiments on four famous
benchmarks in image retrieval tasks demonstrate that Proxy Synthesis
significantly boosts the performance of proxy-based losses and achieves
state-of-the-art performance.
