1 research outputs found
Analyzing Stability of Convolutional Neural Networks in the Frequency Domain
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.Comment: Under review as a conference paper at ICLR2016, minor changes in the
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