  Many existing interpretation methods of convolutional neural networks (CNNs)
mainly analyze in spatial domain, yet model interpretability in frequency
domain has been rarely studied. To the best of our knowledge, there is no study
on the interpretation of modern CNNs from the perspective of the frequency
proportion of filters. In this work, we analyze the frequency properties of
filters in the first layer as it is the entrance of information and relatively
more convenient for analysis. By controlling the proportion of different
frequency filters in the training stage, the network classification accuracy
and model robustness is evaluated and our results reveal that it has a great
impact on the robustness to common corruptions. Moreover, a learnable
modulation of frequency proportion with perturbation in power spectrum is
proposed from the perspective of frequency domain. Experiments on CIFAR-10-C
show 10.97% average robustness gains for ResNet-18 with negligible natural
accuracy degradation.
