DK-CNNs: Dynamic kernel convolutional neural networks

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.

Automated summary

This paper introduces dynamic kernel convolutional neural networks (DK-CNNs) and explains how they enhance the expressive capacity of convolutional operations by extending a latent dimension. DK convolution analyzes fixed features with a latent variable, leading to better performance compared to regular CNNs.