Filter pruning has attracted increasing attention in recent years for its capacity in compressing and accelerating convolutional neural networks. Various data-independent criteria, including norm-based and relationship-based ones, were proposed to prune the most unimportant filters. However, these state-of-the-art criteria fail to fully consider the dissimilarity of filters, and thus might lead to performance degradation. In this paper, we first analyze the limitation of relationship-based criteria with examples, and then introduce a new data-independent criterion, Weighted Hybrid Criterion (WHC), to tackle the problems of both norm-based and relationship-based criteria. By taking the magnitude of each filter and the linear dependence between filters into consideration, WHC can robustly recognize the most redundant filters, which can be safely pruned without introducing severe performance degradation to networks. Extensive pruning experiments in a simple one-shot manner demonstrate the effectiveness of the proposed WHC. In particular, WHC can prune ResNet-50 on ImageNet with more than 42% of floating point operations reduced without any performance loss in top-5 accuracy.

翻译：近年来,过滤器的运行因其压缩和加速进化神经网络的能力而引起越来越多的关注。各种数据独立标准,包括基于规范和基于关系的标准,都建议采用最不重要的过滤器。然而,这些最先进的标准未能充分考虑到过滤器的不相似性,从而可能导致性能退化。在本文件中,我们首先用实例分析基于关系的标准的局限性,然后采用一种新的数据独立标准,即WHC,以解决基于规范和基于关系的标准的问题。通过考虑每个过滤器的规模和过滤器之间的线性依赖性,WHC可以强有力地识别最多余的过滤器,这些过滤器可以在不给网络带来严重性能退化的情况下安全地进行操纵。简单地用一张照片进行大量运行试验,以证明拟议的WHC的有效性。特别是WHC可以在图像网络上输入一个数据独立标准,即 Weighted ResNet-50,其移动点操作减少42%以上,而没有在最高5级精确性能方面的任何损失。