Pruning the Unimportant or Redundant Filters? Synergy Makes Better
Yucheng Cai, Zhuowen Yin, Kailing Guo, Xiangmin Xu · 2021
Filter pruning is a hot topic in convolutional neural network compression due to its friendliness to hardware implementation. Most pruning methods prune filters according to their importance, i.e., removing the filters that have little effect on the final performance of the network. While from another perspective, some recent works propose to prune upon the redundancy of filters. Filters pruned in this way usually have non-negligible effects on the final performance, whereas those effects could be compensated by the remaining filters. Since importance and redundancy pruning respectively captures the local and global information of a convolutional layer, which are mutually complementary to some extent, we propose a new pruning criterion that synergies both of those previous pruning criteria to make full use of the filter information. Comprehensive experiments on benchmark image classification datasets show the effectiveness of our proposed pruning criterion.