宽卷积神经网络的通道中多网格架构

我们提出了一种多网格方法，该方法可以对抗参数卷积相对于标准卷积神经网络（CNN）中通道数的二次增长。已经表明，在标准CNN中存在冗余，因为卷积运算符稀疏的网络可以产生与完整网络相似的性能。..

Multigrid-in-Channels Architectures for Wide Convolutional Neural Networks

We present a multigrid approach that combats the quadratic growth of the number of parameters with respect to the number of channels in standard convolutional neural networks (CNNs). It has been shown that there is a redundancy in standard CNNs, as networks with much sparser convolution operators can yield similar performance to full networks.The sparsity patterns that lead to such behavior, however, are typically random, hampering hardware efficiency. In this work, we present a multigrid-in-channels approach for building CNN architectures that achieves full coupling of the channels, and whose number of parameters is linearly proportional to the width of the network. To this end, we replace each convolution layer in a generic CNN with a multilevel layer consisting of structured (i.e., grouped) convolutions. Our examples from supervised image classification show that applying this strategy to residual networks and MobileNetV2 considerably reduces the number of parameters without negatively affecting accuracy. Therefore, we can widen networks without dramatically increasing the number of parameters or operations.