# Kervolutional Neural Networks

## TL;DR

Kervolutional Neural Networks introduce a kernel-based convolution operation that enhances model capacity and captures complex feature interactions without extra parameters, leading to improved accuracy and convergence in computer vision tasks.

## Contribution

The paper proposes kervolution, a novel kernel-based convolution operation, to extend CNN capabilities by modeling higher-order feature interactions without additional parameters.

## Key findings

- KNN achieve higher accuracy than baseline CNNs.
- KNN demonstrate faster convergence during training.
- KNN effectively model complex feature interactions.

## 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.