  Convolution neural networks have achieved remarkable performance in many
tasks of computing vision. However, CNN tends to bias to low frequency
components. They prioritize capturing low frequency patterns which lead them
fail when suffering from application scenario transformation. While adversarial
example implies the model is very sensitive to high frequency perturbations. In
this paper, we introduce a new regularization method by constraining the
frequency spectra of the filter of the model. Different from band-limit
training, our method considers the valid frequency range probably entangles in
different layers rather than continuous and trains the valid frequency range
end-to-end by backpropagation. We demonstrate the effectiveness of our
regularization by (1) defensing to adversarial perturbations; (2) reducing the
generalization gap in different architecture; (3) improving the generalization
ability in transfer learning scenario without fine-tune.
