Ulicny, Matej and Krylov, Vladimir A and Dahyot, Rozenn
(2019)
Harmonic Networks with Limited Training Samples.
2019 27th European Signal Processing Conference (EUSIPCO).
pp. 1-5.
ISSN 2076-1465
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.
| Item Type: |
Article
|
| Keywords: |
Lapped Discrete Cosine Transform; harmonic
network; convolutional filter; limited data; |
| Academic Unit: |
Faculty of Science and Engineering > Computer Science |
| Item ID: |
15158 |
| Identification Number: |
https://doi.org/10.23919/EUSIPCO.2019.8902831 |
| Depositing User: |
Rozenn Dahyot
|
| Date Deposited: |
20 Dec 2021 12:59 |
| Journal or Publication Title: |
2019 27th European Signal Processing Conference (EUSIPCO) |
| Publisher: |
IEEE |
| Refereed: |
Yes |
| URI: |
|
| Use Licence: |
This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available
here |
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