Kervolutional Neural Networks

Chen Wang, Jianfei Yang, Lihua Xie, Junsong Yuan; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 31-40

Abstract


Convolutional neural networks (CNNs) have enabled the state-of-the-art performance in many computer vision tasks. However, little effort has been devoted to establishing convolution in non-linear space. Existing works mainly leverage on the activation layers, which can only provide point-wise non-linearity. To solve this problem, a new operation, kervolution (kernel convolution), is introduced to approximate complex behaviors of human perception systems leveraging on the kernel trick. It generalizes convolution, enhances the model capacity, and captures higher order interactions of features, via patch-wise kernel functions, but without introducing additional parameters. Extensive experiments show that kervolutional neural networks (KNN) achieve higher accuracy and faster convergence than baseline CNN.

Related Material


[pdf]
[bibtex]
@InProceedings{Wang_2019_CVPR,
author = {Wang, Chen and Yang, Jianfei and Xie, Lihua and Yuan, Junsong},
title = {Kervolutional Neural Networks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}