  In recent years, considerable progress has been made in the visual quality of
Generative Adversarial Networks (GANs). Even so, these networks still suffer
from degradation in quality for high-frequency content, stemming from a
spectrally biased architecture, and similarly unfavorable loss functions. To
address this issue, we present a novel general-purpose Style and WAvelet based
GAN (SWAGAN) that implements progressive generation in the frequency domain.
SWAGAN incorporates wavelets throughout its generator and discriminator
architectures, enforcing a frequency-aware latent representation at every step
of the way. This approach yields enhancements in the visual quality of the
generated images, and considerably increases computational performance. We
demonstrate the advantage of our method by integrating it into the SyleGAN2
framework, and verifying that content generation in the wavelet domain leads to
higher quality images with more realistic high-frequency content. Furthermore,
we verify that our model's latent space retains the qualities that allow
StyleGAN to serve as a basis for a multitude of editing tasks, and show that
our frequency-aware approach also induces improved downstream visual quality.
