Currently, salience-based channel pruning makes continuous breakthroughs in
network compression. In the realization, the salience mechanism is used as a
metric of channel salience to guide pruning. Therefore, salience-based channel
pruning can dynamically adjust the channel width at run-time, which provides a
flexible pruning scheme. However, there are two problems emerging: a gating
function is often needed to truncate the specific salience entries to zero,
which destabilizes the forward propagation; dynamic architecture brings more
cost for indexing in inference which bottlenecks the inference speed. In this
paper, we propose a Progressive Channel-Shrinking (PCS) method to compress the
selected salience entries at run-time instead of roughly approximating them to
zero. We also propose a Running Shrinking Policy to provide a testing-static
pruning scheme that can reduce the memory access cost for filter indexing. We
evaluate our method on ImageNet and CIFAR10 datasets over two prevalent
networks: ResNet and VGG, and demonstrate that our PCS outperforms all
baselines and achieves state-of-the-art in terms of compression-performance
tradeoff. Moreover, we observe a significant and practical acceleration of
inference.

本文提出了一种逐步通道压缩（PCS）方法，以在运行时动态压缩选定的显著度条目，代替粗略地将它们近似为零。并提出一种运行缩小策略，提供一种测试静态的修剪方案，可以减少滤波器索引的内存访问成本，结果表明 PCS 在图像分类任务中超越了所有基线，并在压缩性能平衡方面（tradeoff）达到了最新的性能，实现了显著的推理加速。