  Recently, the generalization behavior of Convolutional Neural Networks (CNN)
is gradually transparent through explanation techniques with the frequency
components decomposition. However, the importance of the phase spectrum of the
image for a robust vision system is still ignored. In this paper, we notice
that the CNN tends to converge at the local optimum which is closely related to
the high-frequency components of the training images, while the amplitude
spectrum is easily disturbed such as noises or common corruptions. In contrast,
more empirical studies found that humans rely on more phase components to
achieve robust recognition. This observation leads to more explanations of the
CNN's generalization behaviors in both robustness to common perturbations and
out-of-distribution detection, and motivates a new perspective on data
augmentation designed by re-combing the phase spectrum of the current image and
the amplitude spectrum of the distracter image. That is, the generated samples
force the CNN to pay more attention to the structured information from phase
components and keep robust to the variation of the amplitude. Experiments on
several image datasets indicate that the proposed method achieves
state-of-the-art performances on multiple generalizations and calibration
tasks, including adaptability for common corruptions and surface variations,
out-of-distribution detection, and adversarial attack.
