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arXiv · 1811.08390

Structured Pruning for Efficient ConvNets via Incremental Regularization

Huan Wang·Qiming Zhang·Yuehai Wang·Haoji Hu
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Abstract

Parameter pruning is a promising approach for CNN compression and acceleration by eliminating redundant model parameters with tolerable performance loss. Despite its effectiveness, existing regularization-based parameter pruning methods usually drive weights towards zero with large and constant regularization factors, which neglects the fact that the expressiveness of CNNs is fragile and needs a more gentle way of regularization for the networks to adapt during pruning. To solve this problem, we propose a new regularization-based pruning method (named IncReg) to incrementally assign different regularization factors to different weight groups based on their relative importance, whose effectiveness is proved on popular CNNs compared with state-of-the-art methods.

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Huan Wang, Qiming Zhang, Yuehai Wang, Haoji Hu. 2018-11-20. Structured Pruning for Efficient ConvNets via Incremental Regularization. https://arxiv.org/abs/1811.08390

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