One-shot Network Pruning at Initialization (OPaI) is an effective method to
decrease network pruning costs. Recently, there is a growing belief that data
is unnecessary in OPaI. However, we obtain an opposite conclusion by ablation
experiments in two representative OPaI methods, SNIP and GraSP. Specifically,
we find that informative data is crucial to enhancing pruning performance. In
this paper, we propose two novel methods, Discriminative One-shot Network
Pruning (DOP) and Super Stitching, to prune the network by high-level visual
discriminative image patches. Our contributions are as follows. (1) Extensive
experiments reveal that OPaI is data-dependent. (2) Super Stitching performs
significantly better than the original OPaI method on benchmark ImageNet,
especially in a highly compressed model.

本文提出了两种新的神经网络稀疏化方法：针对高级视觉特征进行剪枝的 DOP 方法和 Super Stitching 方法。通过大量实验发现，One-shot Network Pruning 需要数据的支持，并且 Super Stitching 在高度压缩的模型上具有显著的优势。