论文标题
重量依赖网络修剪的门
Weight-dependent Gates for Network Pruning
论文作者
论文摘要
在本文中，提出了一个简单而有效的网络修剪框架，以同时解决修剪指标，修剪比和效率约束的问题。本文认为，修剪决策应取决于卷积的权重，因此提出了新颖的重量依赖性门（W-gates），以从过滤重量中学习信息，并获得二进制门以修剪或保持过滤器自动保留二进制门。为了修剪效率限制的网络，构建了一个可切换效率模块，以预测候选候选网络的硬件延迟或失败。结合建议的效率模块，W-Gates可以以效率感知的方式进行过滤器修剪，并实现以更高的精度效率折衷的紧凑网络。我们已经证明了提出的方法对RESNET34，RESNET50和MOBILENET V2的有效性，分别实现了高达1.33/1.28/1.1的TOP-1精度，而Imagenet上的硬件延迟较低。与最先进的方法相比，W-Gates也达到了卓越的性能。
In this paper, a simple yet effective network pruning framework is proposed to simultaneously address the problems of pruning indicator, pruning ratio, and efficiency constraint. This paper argues that the pruning decision should depend on the convolutional weights, and thus proposes novel weight-dependent gates (W-Gates) to learn the information from filter weights and obtain binary gates to prune or keep the filters automatically. To prune the network under efficiency constraints, a switchable Efficiency Module is constructed to predict the hardware latency or FLOPs of candidate pruned networks. Combined with the proposed Efficiency Module, W-Gates can perform filter pruning in an efficiency-aware manner and achieve a compact network with a better accuracy-efficiency trade-off. We have demonstrated the effectiveness of the proposed method on ResNet34, ResNet50, and MobileNet V2, respectively achieving up to 1.33/1.28/1.1 higher Top-1 accuracy with lower hardware latency on ImageNet. Compared with state-of-the-art methods, W-Gates also achieves superior performance.
