  We propose a simple, yet powerful regularization technique that can be used
to significantly improve both the pairwise and triplet losses in learning local
feature descriptors. The idea is that in order to fully utilize the expressive
power of the descriptor space, good local feature descriptors should be
sufficiently "spread-out" over the space. In this work, we propose a
regularization term to maximize the spread in feature descriptor inspired by
the property of uniform distribution. We show that the proposed regularization
with triplet loss outperforms existing Euclidean distance based descriptor
learning techniques by a large margin. As an extension, the proposed
regularization technique can also be used to improve image-level deep feature
embedding.
