我们提出了一种多通道网格（MGIC）方法，该方法可解决参数数量相对于标准卷积神经网络（CNN）中通道数量的二次增长。已经表明，在标准CNN中存在冗余，因为具有轻型卷积运算符或稀疏卷积运算符的网络产生的性能与完整网络相似。..

Multigrid-in-Channels Neural Network Architectures

We present a multigrid-in-channels (MGIC) approach that tackles 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 light or sparse convolution operators yield similar performance to full networks.However, the number of parameters in the former networks also scales quadratically in width, while in the latter case, the parameters typically have random sparsity patterns, hampering hardware efficiency. Our approach for building CNN architectures scales linearly with respect to the network's width while retaining full coupling of the channels as in standard CNNs. To this end, we replace each convolution block with its MGIC block utilizing a hierarchy of lightweight convolutions. Our extensive experiments on image classification, segmentation, and point cloud classification show that applying this strategy to different architectures like ResNet and MobileNetV3 considerably reduces the number of parameters while obtaining similar or better accuracy. For example, we obtain 76.1% top-1 accuracy on ImageNet with a lightweight network with similar parameters and FLOPs to MobileNetV3.