Many current convolutional neural networks are hard to meet the practical application requirement because of the enormous network parameters. For accelerating the inference speed of networks, more and more attention has been paid to network compression. Network pruning is one of the most efficient and simplest ways to compress and speed up the networks. In this paper, a pruning algorithm for the lightweight task is proposed, and a pruning strategy based on feature representation is investigated. Different from other pruning approaches, the proposed strategy is guided by the practical task and eliminates the irrelevant filters in the network. After pruning, the network is compacted to a smaller size and is easy to recover accuracy with fine-tuning. The performance of the proposed pruning algorithm is validated on the acknowledged image datasets, and the experimental results prove that the proposed algorithm is more suitable to prune the irrelevant filters for the fine-tuning dataset.

中文翻译：

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计算机视觉中轻量级任务的小型网络：一种基于特征表示的剪枝方法

目前很多卷积神经网络由于网络参数庞大，难以满足实际应用需求。为了加快网络的推理速度，网络压缩越来越受到重视。网络修剪是压缩和加速网络的最有效和最简单的方法之一。本文提出了一种轻量级任务的剪枝算法，并研究了一种基于特征表示的剪枝策略。与其他剪枝方法不同，所提出的策略以实际任务为指导，并消除了网络中不相关的过滤器。剪枝后，网络被压缩到更小的尺寸，并且通过微调很容易恢复精度。所提出的剪枝算法的性能在公认的图像数据集上得到验证，