# Studying Stability of Different Convolutional Neural Networks Against Additive Noise

### Hamed H. Aghdam, Elnaz J. Heravi, Domenec Puig

#### Abstract

Understanding internal process of ConvNets is commonly done using visualization techniques. However, these techniques do not usually provide a tool for estimating stability of a ConvNet against noise. In this paper, we show how to analyze a ConvNet in the frequency domain. Using the frequency domain analysis, we show the reason that a ConvNet might be sensitive to a very low magnitude additive noise. Our experiments on a few ConvNets trained on different datasets reveals that convolution kernels of a trained ConvNet usually pass most of the frequencies and they are not able to effectively eliminate the effect of high frequencies.They also show that a convolution kernel with more concentrated frequency response is more stable against noise. Finally, we illustrate that augmenting a dataset with noisy images can compress the frequency response of convolution kernels.

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#### Paper Citation

#### in Harvard Style

Aghdam H., J. Heravi E. and Puig D. (2017). **Studying Stability of Different Convolutional Neural Networks Against Additive Noise** . In *Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)* ISBN 978-989-758-226-4, pages 362-369. DOI: 10.5220/0006200003620369

#### in Bibtex Style

@conference{visapp17,

author={Hamed H. Aghdam and Elnaz J. Heravi and Domenec Puig},

title={Studying Stability of Different Convolutional Neural Networks Against Additive Noise},

booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)},

year={2017},

pages={362-369},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0006200003620369},

isbn={978-989-758-226-4},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)

TI - Studying Stability of Different Convolutional Neural Networks Against Additive Noise

SN - 978-989-758-226-4

AU - Aghdam H.

AU - J. Heravi E.

AU - Puig D.

PY - 2017

SP - 362

EP - 369

DO - 10.5220/0006200003620369