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arXiv · 1804.09461

Structured Pruning for Efficient ConvNets via Incremental Regularization

Huan Wang·Qiming Zhang·Yuehai Wang·Yu Lu·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 degrade. Despite its effectiveness, existing regularization-based parameter pruning methods usually drive weights towards zero with large and constant regularization factors, which neglects the fragility of the expressiveness of CNNs, and thus calls for a more gentle regularization scheme so that the networks can adapt during pruning. To achieve this, we propose a new and novel regularization-based pruning method, named IncReg, to incrementally assign different regularization factors to different weights based on their relative importance. Empirical analysis on CIFAR-10 dataset verifies the merits of IncReg. Further extensive experiments with popular CNNs on CIFAR-10 and ImageNet datasets show that IncReg achieves comparable to even better results compared with state-of-the-arts. Our source codes and trained models are available here: https://github.com/mingsun-tse/caffe_increg.

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Huan Wang, Qiming Zhang, Yuehai Wang, Yu Lu, Haoji Hu. 2018-04-25. Structured Pruning for Efficient ConvNets via Incremental Regularization. https://arxiv.org/abs/1804.09461

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