论文标题
重生：通过代表性选举进行过滤器修剪
REPrune: Filter Pruning via Representative Election
论文作者
论文摘要
即使基于规范的过滤器修剪方法已被广泛接受，但“较小的少数重要”标准是否在确定修剪过滤器方面是最佳的。尤其是当我们只保留一小部分原始过滤器时，无论规范值如何，选择可以最好地代表整个过滤器的过滤器更为重要。我们的新颖修剪方法名为“重新恢复”，通过通过聚类选择代表性过滤器来解决此问题。通过从一个相似过滤器的簇中选择一个过滤器，并避免选择相邻的大滤镜，重新释放可以以相似的精度实现更好的压缩率。我们的方法还更快地恢复了准确性，并且需要在微调过程中较小的过滤器转移。从经验上讲，再生可降低49％以上的失败，CIFAR-10的RESNET-11的准确性增长0.53％。同样，重新降低了超过41.8％的失败，而ImageNet的RESNET-18验证损失为1.67％。
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.
