Pruning has become a very powerful and effective technique to compress and
accelerate modern neural networks. Existing pruning methods can be grouped into
two categories: filter pruning (FP) and weight pruning (WP). FP wins at
hardware compatibility but loses at the compression ratio compared with WP. To
converge the strength of both methods, we propose to prune the filter in the
filter. Specifically, we treat a filter $F \in \mathbb{R}^{C\times K\times K}$
as $K \times K$ stripes, i.e., $1\times 1$ filters $\in \mathbb{R}^{C}$, then
by pruning the stripes instead of the whole filter, we can achieve finer
granularity than traditional FP while being hardware friendly. We term our
method as SWP (\emph{Stripe-Wise Pruning}). SWP is implemented by introducing a
novel learnable matrix called Filter Skeleton, whose values reflect the shape
of each filter. As some recent work has shown that the pruned architecture is
more crucial than the inherited important weights, we argue that the
architecture of a single filter, i.e., the shape, also matters. Through
extensive experiments, we demonstrate that SWP is more effective compared to
the previous FP-based methods and achieves the state-of-art pruning ratio on
CIFAR-10 and ImageNet datasets without obvious accuracy drop. Code is available
at this https URL

本文提出了一种名为 SWP 的，基于 stripes 的滤波器修剪方法，该方法实现了高精度压缩和加速现代神经网络，大大提高了传统滤波器修剪方法的硬件友好性和压缩比，并通过 CIFAR-10 和 ImageNet 数据集的实验表明了其卓越的火炬率和准确度。