Efficient CNN Architecture Design Guided by Visualization. (arXiv:2207.10318v1 [cs.CV])

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. …

architecture arxiv cnn cv design visualization