ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Introduction
 Centre loss penalises the distance between the deep features and their corresponding class centers in Euclidean space to achieve intraclass compactness.
 SphereFace assumes the linear transformation matrix in the last fully connected layer can be used as a representation of the class centers in an angular space and pensalises the angles between the deep features and their corresponding weights in a multiplicative way.
 ArcFace is an additive angular margin loss.
Architecture
Softmax Loss
 Softmax loss doesn’t enforce higher similarity for intraclass samples and diversity for interclass samples. (See figure below)
ArcFace Loss

We change W.x as W x cos\theta and set bias as 0. 
If we normalize W as a unit vector, then W becomes 1.  We also normalize x and scale it to s.
Results
Kaushik Rangadurai
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