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Recent advances in convolutional neural networks(CNNs) usually come with theexpense of excessive computational overhead and memory footprint. Networkcompression aims to alleviate this issue by training compact models withcomparable performance. However, existing compression techniques either entaildedicated expert design or compromise with a moderate performance drop. In thispaper, we propose a novel structured sparsification method for efficientnetwork compression. The proposed method automatically induces structuredsparsity on the convolutional weights, thereby facilitating the implementationof the compressed model with the highly-optimized group convolution. We furtheraddress the problem of inter-group communication with a learnable channelshuffle mechanism. The proposed approach can be easily applied to compress manynetwork architectures with a negligible performance drop. Extensiveexperimental results and analysis demonstrate that our approach gives acompetitive performance against the recent network compression counterpartswith a sound accuracy-complexity trade-off.

Structured Sparsification with Joint Optimization of Group Convolution and Channel Shuffle