  Group convolution works well with many deep convolutional neural networks
(CNNs) that can effectively compress the model by reducing the number of
parameters and computational cost. Using this operation, feature maps of
different group cannot communicate, which restricts their representation
capability. To address this issue, in this work, we propose a novel operation
named Hierarchical Group Convolution (HGC) for creating computationally
efficient neural networks. Different from standard group convolution which
blocks the inter-group information exchange and induces the severe performance
degradation, HGC can hierarchically fuse the feature maps from each group and
leverage the inter-group information effectively. Taking advantage of the
proposed method, we introduce a family of compact networks called HGCNets.
Compared to networks using standard group convolution, HGCNets have a huge
improvement in accuracy at the same model size and complexity level. Extensive
experimental results on the CIFAR dataset demonstrate that HGCNets obtain
significant reduction of parameters and computational cost to achieve
comparable performance over the prior CNN architectures designed for mobile
devices such as MobileNet and ShuffleNet.
