Studying stability of different convolutional neural networks against additive noise

Hamed Habibi Aghdam, Elnaz Jahani Heravi, Domenec Puig

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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. Copyright © 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.
Original languageEnglish
Title of host publication12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2017; Porto; Portugal
Publication statusPublished - 2017

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