A Complex-Valued VGG Network Based Deep Learing Algorithm for Image Recognition

At present, many deep neural networks are applied to image recognition. But most of them are based on real-valued operations and represents. Since the algorithm of complex operation has been put forward, we apply the VGG model to the complex domain in the paper. We provide the advantages which the complex-valued network possesses in terms of the depth and width of networks by calculating. When possess the same parameters, the complex-valued network is deeper and wider than the real-valued network. We test both complex-valued VGG network and real-valued one on image recognition. Experiments show that the complex-valued VGG network has better performance comparing with the traditional real-valued VGG network in terms of stability and convergence speed.