  Convolutional Neural Networks (CNNs) have been widely used in computer vision
tasks, such as face recognition and verification, and have achieved
state-of-the-art results due to their ability to capture discriminative deep
features. Conventionally, CNNs have been trained with softmax as supervision
signal to penalize the classification loss. In order to further enhance the
discriminative capability of deep features, we introduce a joint supervision
signal, Git loss, which leverages on softmax and center loss functions. The aim
of our loss function is to minimize the intra-class variations as well as
maximize the inter-class distances. Such minimization and maximization of deep
features are considered ideal for face recognition task. We perform experiments
on two popular face recognition benchmarks datasets and show that our proposed
loss function achieves maximum separability between deep face features of
different identities and achieves state-of-the-art accuracy on two major face
recognition benchmark datasets: Labeled Faces in the Wild (LFW) and YouTube
Faces (YTF). However, it should be noted that the major objective of Git loss
is to achieve maximum separability between deep features of divergent
identities.
