TbsNet: the importance of thin-branch structures in CNNs.

ABSTRACT: The performance of a convolutional neural network (CNN) model is influenced by several factors, such as depth, width, network structure, size of the receptive field, and feature map scaling. The optimization of the best combination of these factors poses as the main difficulty in designing a viable architecture. This article presents an analysis of key factors influencing network performance, offers several strategies for constructing an efficient convolutional network, and introduces a novel architecture named TbsNet (thin-branch structure network). In order to minimize computation costs and feature redundancy, lightweight operators such as asymmetric convolution, pointwise convolution, depthwise convolution, and group convolution are implemented to further reduce the network's weight. Un

SUBMITTER: Hu X 

PROVIDER: S-EPMC10280644 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature