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

Interleaved Group Convolutions for Deep Neural Networks