Abstract
We motivate and present Ring loss, a simple and elegant
feature normalization approach for deep networks designed
to augment standard loss functions such as Softmax. We
argue that deep feature normalization is an important aspect of supervised classification problems where we require
the model to represent each class in a multi-class problem
equally well. The direct approach to feature normalization through the hard normalization operation results in a
non-convex formulation. Instead, Ring loss applies soft normalization, where it gradually learns to constrain the norm
to the scaled unit circle while preserving convexity leading
to more robust features. We apply Ring loss to large-scale
face recognition problems and present results on LFW, the
challenging protocols of IJB-A Janus, Janus CS3 (a superset of IJB-A Janus), Celebrity Frontal-Profile (CFP) and
MegaFace with 1 million distractors. Ring loss outperforms
strong baselines, matches state-of-the-art performance on
IJB-A Janus and outperforms all other results on the challenging Janus CS3 thereby achieving state-of-the-art. We
also outperform strong baselines in handling extremely low
resolution face matching