  With the development of convolutional neural networks (CNNs) in recent years,
the network structure has become more and more complex and varied, and has
achieved very good results in pattern recognition, image classification, object
detection and tracking. For CNNs used for image classification, in addition to
the network structure, more and more research is now focusing on the
improvement of the loss function, so as to enlarge the inter-class feature
differences, and reduce the intra-class feature variations as soon as possible.
Besides the traditional Softmax, typical loss functions include L-Softmax,
AM-Softmax, ArcFace, and Center loss, etc. Based on the concept of predefined
evenly-distributed class centroids (PEDCC) in CSAE network, this paper proposes
a PEDCC-based loss function called PEDCC-Loss, which can make the inter-class
distance maximal and intra-class distance small enough in hidden feature space.
Multiple experiments on image classification and face recognition have proved
that our method achieve the best recognition accuracy, and network training is
stable and easy to converge. Code is available in
https://github.com/ZLeopard/PEDCC-Loss
