Complex-Valued Densely Connected Convolutional Networks.

In recent years, deep learning has made significant progress in computer vision. However, most studies focus on the algorithms in real field. Complex number can have richer characterization capabilities and use fewer training parameters. This paper proposes a complex-valued densely connected convolutional network, which is complex-valued DenseNets. It generalizes the network structure of real-valued DenseNets to complex field and constructs the basic architectures including complex dense block and complex transition layers. Experiments were performed on the CIFAR-10 database and CIFAR-100 datasets. Experimental results show the proposed algorithm has a lower error rate and fewer parameters than real-valued DenseNets and complex-valued ResNets.