Channel pruning is broadly recognized as an effective approach to obtain a
small compact model through eliminating unimportant channels from a large
cumbersome network. Contemporary methods typically perform iterative pruning
procedure from the original over-parameterized model, which is both tedious and
expensive especially when the pruning is aggressive. In this paper, we propose
a simple yet effective channel pruning technique, termed network Pruning via
rEsource rEalLocation (PEEL), to quickly produce a desired slim model with
negligible cost. Specifically, PEEL first constructs a predefined backbone and
then conducts resource reallocation on it to shift parameters from less
informative layers to more important layers in one round, thus amplifying the
positive effect of these informative layers. To demonstrate the effectiveness
of PEEL , we perform extensive experiments on ImageNet with ResNet-18,
ResNet-50, MobileNetV2, MobileNetV3-small and EfficientNet-B0. Experimental
results show that structures uncovered by PEEL exhibit competitive performance
with state-of-the-art pruning algorithms under various pruning settings. Our
code is available at this https URL

本文介绍了一种名为 PEEL 的通道剪枝技术，通过资   源重定位实现了快速生成精简模型的目标，实验结果表明，PEEL 前所未有地提高了信息层的    正面效应，使得剥离比较无关紧要的结构在各种剪枝设置下都能够达到与最先进的剪枝算法相   竞争的性能。