  Replacing normal convolutions with group convolutions can significantly
increase the computational efficiency of modern deep convolutional networks,
which has been widely adopted in compact network architecture designs. However,
existing group convolutions undermine the original network structures by
cutting off some connections permanently resulting in significant accuracy
degradation. In this paper, we propose dynamic group convolution (DGC) that
adaptively selects which part of input channels to be connected within each
group for individual samples on the fly. Specifically, we equip each group with
a small feature selector to automatically select the most important input
channels conditioned on the input images. Multiple groups can adaptively
capture abundant and complementary visual/semantic features for each input
image. The DGC preserves the original network structure and has similar
computational efficiency as the conventional group convolution simultaneously.
Extensive experiments on multiple image classification benchmarks including
CIFAR-10, CIFAR-100 and ImageNet demonstrate its superiority over the existing
group convolution techniques and dynamic execution methods. The code is
available at https://github.com/zhuogege1943/dgc.
