Abstract
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.
Abstract (translated)
近年来，随着卷积神经网络（CNN）的发展，其网络结构越来越复杂多变，在模式识别、图像分类、目标检测和跟踪等方面取得了很好的效果。对于用于图像分类的CNN，除了网络结构外，目前越来越多的研究集中在损耗函数的改进上，以扩大类间特征差异，并尽快减小类内特征变化。除了传统的SoftMax外，典型的损失函数还包括L-SoftMax、AM-SoftMax、ArcFace和Center Loss等，本文基于CSAE网络中预先定义的均匀分布类形心（pedcc）的概念，提出了一种基于pedcc的损失函数pedcc loss，它可以使类间距离最大，类内距离最大。NCE在隐藏的特征空间足够小。通过对图像分类和人脸识别的多次实验，证明了该方法具有最佳的识别精度，网络训练稳定，易于收敛。
URL
https://arxiv.org/abs/1904.06008
