  Data augmentation in feature space is effective to increase data diversity.
Previous methods assume that different classes have the same covariance in
their feature distributions. Thus, feature transform between different classes
is performed via translation. However, this approach is no longer valid for
recent deep metric learning scenarios, where feature normalization is widely
adopted and all features lie on a hypersphere.
  This work proposes a novel spherical feature transform approach. It relaxes
the assumption of identical covariance between classes to an assumption of
similar covariances of different classes on a hypersphere. Consequently, the
feature transform is performed by a rotation that respects the spherical data
distributions. We provide a simple and effective training method, and in depth
analysis on the relation between the two different transforms. Comprehensive
experiments on various deep metric learning benchmarks and different baselines
verify that our method achieves consistent performance improvement and
state-of-the-art results.
