Flow group convolution: group convolution with channels information interaction
Bohan Li, Zuguo He, Xiang ye, Fengnan Li, Yong Li · 2021
Group convolution can significantly reduce the computational cost by dividing the feature map channels into groups and then convolution operation is applied within each group. However, evenly dividing channels will cause the isolation between divided groups, i.e., no interaction between groups. To address the channel isolation problem, we proposed flow group convolution (FGConv) that utilizes different but overlapped input channels to compute output channels and enhance the interaction of different groups. FGConv can be easily applied to the existing networks and reduce their computational cost. We replace the original convolution with FGConv in ResNets and validate them on CIFAR-10 and CIFAR-100 benchmarks. Experiments results demonstrate that FGConv performs better than existing group convolution techniques.