Understanding the internal process of ConvNets is commonly done using visualization techniques. However, these techniques do not usually provide a tool for estimating the stability of a ConvNet against noise. In this paper, we show how to analyze a ConvNet in the frequency domain using a 4-dimensional visualization technique. 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 revealed 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. Our next experiments shows that a convolution kernel which has a more concentrated frequency response could be more stable. Finally, we show that fine-tuning a ConvNet using a training set augmented with noisy images can produce more stable ConvNets.
@article{arxiv.1511.03042,
title = {Analyzing Stability of Convolutional Neural Networks in the Frequency Domain},
author = {Elnaz J. Heravi and Hamed H. Aghdam and Domenec Puig},
journal= {arXiv preprint arXiv:1511.03042},
year = {2015}
}
Comments
Under review as a conference paper at ICLR2016, minor changes in the text