# Harmonic Networks with Limited Training Samples

## TL;DR

This paper introduces harmonic networks using DCT filters in CNNs, demonstrating their effectiveness in limited training data scenarios and comparing favorably to wavelet-based scattering networks.

## Contribution

It proposes a computationally efficient harmonic block with DCT filters for CNNs, enhancing performance with limited training samples.

## Key findings

- Harmonic networks perform well with limited data.
- DCT-based harmonic blocks compare favorably to wavelet scattering networks.
- Efficient filter design reduces overfitting in CNNs.

## Abstract

Convolutional neural networks (CNNs) are very popular nowadays for image processing. CNNs allow one to learn optimal filters in a (mostly) supervised machine learning context. However this typically requires abundant labelled training data to estimate the filter parameters. Alternative strategies have been deployed for reducing the number of parameters and / or filters to be learned and thus decrease overfitting. In the context of reverting to preset filters, we propose here a computationally efficient harmonic block that uses Discrete Cosine Transform (DCT) filters in CNNs. In this work we examine the performance of harmonic networks in limited training data scenario. We validate experimentally that its performance compares well against scattering networks that use wavelets as preset filters.