SCGNet: efficient sparsely connected group convolution network for wheat grains classification

• 全文链接

SCGNet：用于小麦籽粒分类的高效稀疏连通群卷积网络

关键词：

来源：

Frontiers

全文链接：

//agri.nais.net.cn/topic/downloadFile/35bd4379-f712-48fa-9229-7629dffac94c

来源地址：

https://www.frontiersin.org/articles/10.3389/fpls.2023.1304962/full

资源所属：

小麦遗传育种专题

类型：

前沿资讯

语种：

英语

原文发布日期：

2023-12-22

摘要：

Specifically, our proposed model incorporates several modules that enhance information exchange and feature multiplexing between group convolutions. This mechanism enables the network to gather feature information from each subgroup of the previous layer, facilitating effective utilization of upper-layer features. Additionally, we introduce sparsity in channel connections between groups to further reduce computational complexity without compromising accuracy. Furthermore, we design a novel classification output layer based on 3-D convolution, replacing the traditional maximum pooling layer and fully connected layer in conventional convolutional neural networks (CNNs). This modification results in more efficient classification output generation.