  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.
