DK-CNNs: Dynamic kernel convolutional neural networks

NEUROCOMPUTING

卷:422

Article

Liu, Jialin¹  Chao, Fei^1,3  Lin, Chih-Min²  Zhou, Changle¹  Shang, Changjing³ 

[1] Xiamen Univ, Sch Informat, Dept Artificial Intelligence, Xiamen, Peoples R China

[2] Yuan Ze Univ, Dept Elect Engn, Taoyuan 320, Taiwan

[3] Aberystwyth Univ, Inst Math Phys & Comp Sci, Aberystwyth, Dyfed, Wales

关键词: Deep neural networks;    Convolutional neural networks;    Convolution kernel;   

DOI  :  10.1016/j.neucom.2020.09.005

来源: Elsevier

【 摘 要 】

This paper introduces dynamic kernel convolutional neural networks (DK-CNNs), an enhanced type of CNN, by performing line-by-line scanning regular convolution to generate a latent dimension of kernel weights. The proposed DK-CNN applies regular convolution to the DK weights, which rely on a latent variable, and discretizes the space of the latent variable to extend a new dimension; this process is named DK convolution. DK convolution increases the expressive capacity of the convolution operation without increasing the number of parameters by searching for useful patterns within the new extended dimen-sion. In contrast to conventional convolution, which applies a fixed kernel to analyse the changed features, DK convolution employs a DK to analyse fixed features. In addition, DK convolution can replace a standard convolution layer in any CNN network structure. The proposed DK-CNNs were compared with different network structures with and without a latent dimension on the CIFAR and FashionMNIST data sets. The experimental results show that DK-CNNs can achieve better performance than regular CNNs. (c) 2020 Elsevier B.V. All rights reserved.

【 授权许可】

Free