Ring loss: Convex Feature Normalization for Face Recognition

Understand

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.