Deep convolutional neural networks have been proved successful on a wide
range of tasks, yet they are still hindered by their large computation cost in
many industrial scenarios. In this paper, we propose to reduce such cost for
CNNs through a self-adaptive network pruning method (SANP). Our method
introduces a general Saliency-and-Pruning Module (SPM) for each convolutional
layer, which learns to predict saliency scores and applies pruning for each
channel. Given a total computation budget, SANP adaptively determines the
pruning strategy with respect to each layer and each sample, such that the
average computation cost meets the budget. This design allows SANP to be more
efficient in computation, as well as more robust to datasets and backbones.
Extensive experiments on 2 datasets and 3 backbones show that SANP surpasses
state-of-the-art methods in both classification accuracy and pruning rate.

本文提出了一种通过自适应网络剪枝方法（SANP）来降低卷积神经网络的计算成本。该方法引入了通用的显着性和剪枝模块（SPM）来为每个卷积层预测显着性评分，并且针对每个通道剪枝。在两个数据集和三个骨干的广泛实验中，SANP 在分类准确率和剪枝率方面都超过了最先进的方法。