Even though norm-based filter pruning methods are widely accepted, it is
questionable whether the "smaller-norm-less-important" criterion is optimal in
determining filters to prune. Especially when we can keep only a small fraction
of the original filters, it is more crucial to choose the filters that can best
represent the whole filters regardless of norm values. Our novel pruning method
entitled "REPrune" addresses this problem by selecting representative filters
via clustering. By selecting one filter from a cluster of similar filters and
avoiding selecting adjacent large filters, REPrune can achieve a better
compression rate with similar accuracy. Our method also recovers the accuracy
more rapidly and requires a smaller shift of filters during fine-tuning.
Empirically, REPrune reduces more than 49% FLOPs, with 0.53% accuracy gain on
ResNet-110 for CIFAR-10. Also, REPrune reduces more than 41.8% FLOPs with 1.67%
Top-1 validation loss on ResNet-18 for ImageNet.

本研究提出一种名为 REPrune 的新颖剪枝方法，通过聚类选择代表性滤波器，从而获得更好的压缩率并提高精度。实验证明，REPrune 方法可以在保持相似精度的情况下，减少 49% 的 FLOPs。