  The deep convolutional neural network(CNN) has significantly raised the
performance of image classification and face recognition. Softmax is usually
used as supervision, but it only penalizes the classification loss. In this
paper, we propose a novel auxiliary supervision signal called contrastivecenter
loss, which can further enhance the discriminative power of the features, for
it learns a class center for each class. The proposed contrastive-center loss
simultaneously considers intra-class compactness and inter-class separability,
by penalizing the contrastive values between: (1)the distances of training
samples to their corresponding class centers, and (2)the sum of the distances
of training samples to their non-corresponding class centers. Experiments on
different datasets demonstrate the effectiveness of contrastive-center loss.
