  Deep metric learning aims to learn embeddings that contain semantic
similarity information among data points. To learn better embeddings, methods
to generate synthetic hard samples have been proposed. Existing methods of
synthetic hard sample generation are adopting autoencoders or generative
adversarial networks, but this leads to more hyper-parameters, harder
optimization, and slower training speed. In this paper, we address these
problems by proposing a novel method of synthetic hard sample generation called
symmetrical synthesis. Given two original feature points from the same class,
the proposed method firstly generates synthetic points with each other as an
axis of symmetry. Secondly, it performs hard negative pair mining within the
original and synthetic points to select a more informative negative pair for
computing the metric learning loss. Our proposed method is hyper-parameter free
and plug-and-play for existing metric learning losses without network
modification. We demonstrate the superiority of our proposed method over
existing methods for a variety of loss functions on clustering and image
retrieval tasks. Our implementations is publicly available.
