论文标题
数据不可知的过滤门控，有效的深网
Data Agnostic Filter Gating for Efficient Deep Networks
论文作者
论文摘要
要在低端计算边缘设备上部署训练有素的CNN模型，通常应该在某些计算预算（例如FLOPS）下压缩或修剪模型。当前的过滤器修剪方法主要利用特征图来生成过滤器的重要分数，并修剪较小分数的分数，这些分数忽略了输入批次的差异到稀疏结构在过滤器上的差异。在本文中，我们提出了一种数据不可思议的过滤器修剪方法，该方法使用名为Dagger模块的辅助网络来诱导修剪并采用预审计的权重，以了解每个过滤器的重要性。此外，为了帮助修剪某些FLOP的限制，我们利用明确的拖失式正则化来直接将修剪过滤器推向目标拖球。 CIFAR-10和Imagenet数据集的广泛实验结果表明我们对其他最先进的过滤器修剪方法的优越性。例如，我们的50 \％FLOPS RESNET-50可以在Imagenet数据集上获得76.1 \％TOP-1的精度，超过许多其他过滤器修剪方法。
To deploy a well-trained CNN model on low-end computation edge devices, it is usually supposed to compress or prune the model under certain computation budget (e.g., FLOPs). Current filter pruning methods mainly leverage feature maps to generate important scores for filters and prune those with smaller scores, which ignores the variance of input batches to the difference in sparse structure over filters. In this paper, we propose a data agnostic filter pruning method that uses an auxiliary network named Dagger module to induce pruning and takes pretrained weights as input to learn the importance of each filter. In addition, to help prune filters with certain FLOPs constraints, we leverage an explicit FLOPs-aware regularization to directly promote pruning filters toward target FLOPs. Extensive experimental results on CIFAR-10 and ImageNet datasets indicate our superiority to other state-of-the-art filter pruning methods. For example, our 50\% FLOPs ResNet-50 can achieve 76.1\% Top-1 accuracy on ImageNet dataset, surpassing many other filter pruning methods.
