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Complex-valued Channel-weighted Aggregated Residual Networks for Image Recognition

Convolutional neural networks (CNN) are more and more used in image recognition. Recent studies have shown that complex-valued neural networks have richer representational capability than real-valued neural networks. However, the existing complex-valued CNN still has the following problems: (1) Most of the existing models are two-branch structures; (2) The model does not consider the importance of each complex feature map. In this paper, split-transform-merge and feature recalibration scheme in complex field are proposed, and applied to complex-valued neural networks to form Complex-valued Channel-weighted Aggregated Residual Networks (cSE-ResNeXt). This paper performs the experiments on CIFAR-10 and CIFAR-100 datasets. The experimental results show that the proposed algorithm is superior to both two complex-valued nextworks (ResNeXt and ResNet) and three real-valued nextworks (SE-ResNeXt, ResNeXt and ResNet).