Convolutional Discriminant Analysis

Guoqiang Zhong, Yan Zheng, Xu-Yao Zhang, Hongxu Wei, Xiao Ling

Abstract: Softmax regressor is arguably the most commonly used classifier in convolutional neural networks (CNNs). However, the cross-entropy based softmax loss only supervises the deep neural networks to learn effective representations of data, but does not explicitly enforce the separability between the classes. In this paper, we propose a novel convolutional neural network model, called convolutional discriminative analysis (CDA). Beyond the softmax loss, CDA employs a convolutional discriminant loss (CD-Loss), which minimizes the distance between the sample and its class center while maximizes the distance between the sample and its adversarial class center in the space of the learned deep representations. Extensive experiments on two benchmark data sets, Fashion-MNIST and CIFAR-10, demonstrate the superiority of CDA over traditional deep CNNs on the image classification tasks.