  In this paper, we present a simple and modularized neural network
architecture, named interleaved group convolutional neural networks (IGCNets).
The main point lies in a novel building block, a pair of two successive
interleaved group convolutions: primary group convolution and secondary group
convolution. The two group convolutions are complementary: (i) the convolution
on each partition in primary group convolution is a spatial convolution, while
on each partition in secondary group convolution, the convolution is a
point-wise convolution; (ii) the channels in the same secondary partition come
from different primary partitions. We discuss one representative advantage:
Wider than a regular convolution with the number of parameters and the
computation complexity preserved. We also show that regular convolutions, group
convolution with summation fusion, and the Xception block are special cases of
interleaved group convolutions. Empirical results over standard benchmarks,
CIFAR-$10$, CIFAR-$100$, SVHN and ImageNet demonstrate that our networks are
more efficient in using parameters and computation complexity with similar or
higher accuracy.
