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

F-Drop&Match: GANs with a Dead Zone in the High-Frequency Domain