# Hybrid Cosine Based Convolutional Neural Networks

## TL;DR

This paper introduces Hybrid Cosine Based Convolutional layers that replace standard convolutions with cosine basis functions, reducing parameters and improving training efficiency while maintaining or enhancing classification performance.

## Contribution

The paper proposes a novel convolutional layer using cosine basis functions, enabling faster training, larger receptive fields, and fewer parameters compared to traditional CNN layers.

## Key findings

- Achieves comparable or better accuracy than VGG and ResNet.
- Reduces number of parameters significantly.
- Speeds up convergence during training.

## Abstract

Convolutional neural networks (CNNs) have demonstrated their capability to solve different kind of problems in a very huge number of applications. However, CNNs are limited for their computational and storage requirements. These limitations make difficult to implement these kind of neural networks on embedded devices such as mobile phones, smart cameras or advanced driving assistance systems. In this paper, we present a novel layer named Hybrid Cosine Based Convolution that replaces standard convolutional layers using cosine basis to generate filter weights. The proposed layers provide several advantages: faster convergence in training, the receptive field can be increased at no cost and substantially reduce the number of parameters. We evaluate our proposed layers on three competitive classification tasks where our proposed layers can achieve similar (and in some cases better) performances than VGG and ResNet architectures.