  Deep metric learning aims to learn an embedding space, where semantically
similar samples are close together and dissimilar ones are repelled against. To
explore more hard and informative training signals for augmentation and
generalization, recent methods focus on generating synthetic samples to boost
metric learning losses. However, these methods just use the deterministic and
class-independent generations (e.g., simple linear interpolation), which only
can cover the limited part of distribution spaces around original samples. They
have overlooked the wide characteristic changes of different classes and can
not model abundant intra-class variations for generations. Therefore, generated
samples not only lack rich semantics within the certain class, but also might
be noisy signals to disturb training. In this paper, we propose a novel
intra-class adaptive augmentation (IAA) framework for deep metric learning. We
reasonably estimate intra-class variations for every class and generate
adaptive synthetic samples to support hard samples mining and boost metric
learning losses. Further, for most datasets that have a few samples within the
class, we propose the neighbor correction to revise the inaccurate estimations,
according to our correlation discovery where similar classes generally have
similar variation distributions. Extensive experiments on five benchmarks show
our method significantly improves and outperforms the state-of-the-art methods
on retrieval performances by 3%-6%. Our code is available at
https://github.com/darkpromise98/IAA
