An Expansion Convolution Method Based on Local Region Parameter Sharing
Qimao Yang, Jun Guo · 2019
In this paper, a new convolution method for convolutional neural networks (CNNs) is proposed to improve the accuracy of image classification. To contain more efficient context, some of the parameters in the kernel are selectively expanded so as to be shared by the surrounding pixels. Thus, the convolution filter is enlarged meanwhile the number of the parameters is not increased. Compared to the traditional methods, the proposed method can restrain the over-fitting problem well. The experimental results on benchmarks show that the proposed method can achieve higher accuracies closed to the deeper networks, and get better accuracies in the case of the same network depth.