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，滤波器剪枝因其在压缩和加速卷积神经网络方面的能力而日益受到关注。为修剪最不重要的滤波器，学界提出了多种数据无关的准则，包括基于范数的准则和基于关系的准则。然而，这些先进准则未能充分考虑滤波器间的非相似性，因此可能导致性能下降。本文首先通过实例分析了基于关系准则的局限性，随后引入一种新的数据无关准则——加权混合准则（WHC），以应对基于范数准则和基于关系准则存在的问题。通过综合考虑每个滤波器的幅度以及滤波器之间的线性依赖性，WHC能够稳健地识别出最冗余的滤波器，这些滤波器可在不导致网络性能严重退化的情况下安全剪除。采用简单单次剪枝方式的大量实验证明了所提WHC的有效性。特别地，WHC可在ImageNet上修剪ResNet-50，在top-5准确率无任何损失的前提下减少超过42%的浮点运算量。