Generative adversarial networks built from deep convolutional neural networks
(GANs) lack the ability to exactly replicate the high-frequency components of
natural images. To alleviate this issue, we introduce two novel training
techniques called frequency dropping (F-Drop) and frequency matching (F-Match).
The key idea of F-Drop is to filter out unnecessary high-frequency components
from the input images of the discriminators. This simple modification prevents
the discriminators from being confused by perturbations of the high-frequency
components. In addition, F-Drop makes the GANs focus on fitting in the
low-frequency domain, in which there are the dominant components of natural
images. F-Match minimizes the difference between real and fake images in the
frequency domain for generating more realistic images. F-Match is implemented
as a regularization term in the objective functions of the generators; it
penalizes the batch mean error in the frequency domain. F-Match helps the
generators to fit in the high-frequency domain filtered out by F-Drop to the
real image. We experimentally demonstrate that the combination of F-Drop and
F-Match improves the generative performance of GANs in both the frequency and
spatial domain on multiple image benchmarks.

通过引入频率匹配和频率去除技术，可以训练出更真实的、高品质的图片，从而提高生成对抗网络的性能。