  Modern efficient Convolutional Neural Networks(CNNs) always use Depthwise
Separable Convolutions(DSCs) and Neural Architecture Search(NAS) to reduce the
number of parameters and the computational complexity. But some inherent
characteristics of networks are overlooked. Inspired by visualizing feature
maps and N$\times$N(N$>$1) convolution kernels, several guidelines are
introduced in this paper to further improve parameter efficiency and inference
speed. Based on these guidelines, our parameter-efficient CNN architecture,
called \textit{VGNetG}, achieves better accuracy and lower latency than
previous networks with about 30%$\thicksim$50% parameters reduction. Our
VGNetG-1.0MP achieves 67.7% top-1 accuracy with 0.99M parameters and 69.2%
top-1 accuracy with 1.14M parameters on ImageNet classification dataset.
  Furthermore, we demonstrate that edge detectors can replace learnable
depthwise convolution layers to mix features by replacing the N$\times$N
kernels with fixed edge detection kernels. And our VGNetF-1.5MP archives
64.4%(-3.2%) top-1 accuracy and 66.2%(-1.4%) top-1 accuracy with additional
Gaussian kernels.
