Model-Agnostic Structured Sparsification with Learnable Channel Shuffle

Abstract

Recent advances in convolutional neural networks (CNNs) usually come with the expense of considerable computational overhead and memory footprint. Network compression aims to alleviate this issue by training compact models with comparable performance. However, existing compression techniques either entail dedicated expert design or compromise with a moderate performance drop. To this end, we propose a model-agnostic structured sparsification method for efficient network compression. The proposed method automatically induces structurally sparse representations of the convolutional weights, thereby facilitating the implementation of the compressed model with the highly-optimized group convolution. We further address the problem of inter-group communication with a learnable channel shuffle mechanism. The proposed approach is model-agnostic and highly compressible with a negligible performance drop. Extensive experimental results and analysis demonstrate that our approach performs favorably against the state-of-the-art network pruning methods. The code will be publicly available after the review process.