论文标题
粗粒和细粒自动种植深卷积神经网络
Coarse and fine-grained automatic cropping deep convolutional neural network
论文作者
论文摘要
现有的卷积神经网络修剪算法可以分为两类：粗粒剪切和细粒度的剪裁。本文提出了一种粗糙且细粒度的自动修剪算法，该算法可以实现卷积神经网络的更有效，准确的压缩加速度。首先，将卷积神经网络的中间特征图聚集，以在粗粒剪辑后获得网络结构，然后使用粒子群优化算法进行迭代搜索并优化结构。最后，获得了最佳网络调整子结构。
The existing convolutional neural network pruning algorithms can be divided into two categories: coarse-grained clipping and fine-grained clipping. This paper proposes a coarse and fine-grained automatic pruning algorithm, which can achieve more efficient and accurate compression acceleration for convolutional neural networks. First, cluster the intermediate feature maps of the convolutional neural network to obtain the network structure after coarse-grained clipping, and then use the particle swarm optimization algorithm to iteratively search and optimize the structure. Finally, the optimal network tailoring substructure is obtained.
