  Learning the distance metric between pairs of samples has been studied for
image retrieval and clustering. With the remarkable success of pair-based
metric learning losses, recent works have proposed the use of generated
synthetic points on metric learning losses for augmentation and generalization.
However, these methods require additional generative networks along with the
main network, which can lead to a larger model size, slower training speed, and
harder optimization. Meanwhile, post-processing techniques, such as query
expansion and database augmentation, have proposed the combination of feature
points to obtain additional semantic information. In this paper, inspired by
query expansion and database augmentation, we propose an augmentation method in
an embedding space for pair-based metric learning losses, called embedding
expansion. The proposed method generates synthetic points containing augmented
information by a combination of feature points and performs hard negative pair
mining to learn with the most informative feature representations. Because of
its simplicity and flexibility, it can be used for existing metric learning
losses without affecting model size, training speed, or optimization
difficulty. Finally, the combination of embedding expansion and representative
metric learning losses outperforms the state-of-the-art losses and previous
sample generation methods in both image retrieval and clustering tasks. The
implementation is publicly available.
