[1]姚亮亮,张太红*,张洋宁,等.单参数通道注意力模块[J].计算机技术与发展,2023,33(12):215-220.[doi:10. 3969 / j. issn. 1673-629X. 2023. 12. 030]
　YAO Liang-liang,ZHANG Tai-hong*,ZHANG Yang-ning,et al.Single-parameter Channel Attention Module[J].,2023,33(12):215-220.[doi:10. 3969 / j. issn. 1673-629X. 2023. 12. 030]
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单参数通道注意力模块()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
33
期数:
2023年12期
页码:
215-220
栏目:
人工智能
出版日期:
2023-12-10

文章信息/Info

Title:
Single-parameter Channel Attention Module
文章编号:
1673-629X(2023)12-0215-06
作者:
姚亮亮1; 2; 3 ; 张太红1; 2; 3* ; 张洋宁1; 2; 3 ; 温钊发1; 2; 3
1. 新疆农业大学 计算机与信息工程学院,新疆 乌鲁木齐 830052;
2. 智能农业教育部工程研究中心,新疆 乌鲁木齐 830052;
3. 新疆农业信息化工程技术研究中心,新疆 乌鲁木齐 830052
Author(s):
YAO Liang-liang1; 2; 3 ; ZHANG Tai-hong1; 2; 3* ; ZHANG Yang-ning1; 2; 3 ; WEN Zhao-fa1; 2; 3
1. School of Computer and Information Engineering,Xinjiang Agricultural University,Urumqi 830052,China;
2. Intelligent Agriculture Ministry of Education Engineering Research Center,Urumqi 830052,China;
3. Xinjiang Agricultural Information Engineering Technology Research Center,Urumqi 830052,China
关键词:
卷积神经网络; 深度学习; 通道注意力; 图像分类; 计算量
Keywords:
convolutional neural network; deep learning; channel attention; image classification; amount of calculation
分类号:
TP18
DOI:
10. 3969 / j. issn. 1673-629X. 2023. 12. 030
摘要:
随着深度学习的发展,通道注意力在卷积神经网络上的表征能力上发挥了巨大的作用。 为了进一步加强通道注意力模块在深度神经网络中的作用,针对通道注意力的参数量方面,提出了一
种单参数通道注意力( APA) 模块。 首先,APA 模块在图像通道特征的求和向量上加单参数。 然后,通过度量通道向量和求和向量在方向上的关系,求取通道注意力权重。 最后,经过激活函数(Sigmoid) 激活注意力权重,使其分布更平稳。 与其他通道注意力模块相比,该模块只有微量参数,且该模块的代码实现非常简单。 在数据集 CIFAR-10 与 CIFAR-100 上,使用 APA 模块嵌入
到 MobileNet,ResNet系列主干,与同类方法压缩激励模块( SE) 、有效的通道注意力模块( ECA)进行了实验对比,验证了 APA 模块的有效性。
Abstract:
With the development of deep learning,channel attention has played a huge role in the representation ability of convolutionalneural networks. In order to further strengthen the role of channel attention module in deep neural network,a single parameter channel attention ( APA) module is proposed for the parameter quantity of channel attention. First,the APA module adds a single parameter to theimage channel feature summation vector. Then, the channel attention weight is obtained by measuring the relationship between thechannel vector and the summation vector in the direction. Finally,the attention weight is activated by the activation function (Sigmoid)to make its distribution more stable. Compared with other channel attention modules,the proposed module has only trace parameters,andthe code implementation of the module is largely simple. By embedding the APA module into the MobileNet and ResNet seriesbackbones on the data sets CIFAR-10 and CIFAR-100,it is compared with the similar method Squeeze-and-Excitation module ( SE)and Effective Channel Attention Module ( ECA) which shows the effectiveness of the APA module.

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更新日期/Last Update: 2023-12-10