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.