High-Frequency Component Helps Explain the Generalization of Convolutional Neural Networks

We investigate the relationship between the frequency spectrum of image data and the generalization behavior of convolutional neural networks (CNN). We first notice CNN's ability in capturing the high-frequency components of images. These high-frequency components are almost imperceptible to a human. Thus the observation leads to multiple hypotheses that are related to the generalization behaviors of CNN, including a potential explanation for adversarial examples, a discussion of CNN's trade-off between robustness and accuracy, and some evidence in understanding training heuristics.

1. Introduction

Deep learning has achieved many recent advances in predictive modeling in various tasks, but the community has nonetheless become alarmed by the unintuitive generalization behaviors of neural networks, such as the capacity in memorizing label shuffled data [65] and the vulnerability towards adversarial examples [54], [21]