  In this paper we propose a novel decomposition method based on filter group
approximation, which can significantly reduce the redundancy of deep
convolutional neural networks (CNNs) while maintaining the majority of feature
representation. Unlike other low-rank decomposition algorithms which operate on
spatial or channel dimension of filters, our proposed method mainly focuses on
exploiting the filter group structure for each layer. For several commonly used
CNN models, including VGG and ResNet, our method can reduce over 80%
floating-point operations (FLOPs) with less accuracy drop than state-of-the-art
methods on various image classification datasets. Besides, experiments
demonstrate that our method is conducive to alleviating degeneracy of the
compressed network, which hurts the convergence and performance of the network.
