  The key objective of Generative Adversarial Networks (GANs) is to generate
new data with the same statistics as the provided training data. However,
multiple recent works show that state-of-the-art architectures yet struggle to
achieve this goal. In particular, they report an elevated amount of high
frequencies in the spectral statistics which makes it straightforward to
distinguish real and generated images. Explanations for this phenomenon are
controversial: While most works attribute the artifacts to the generator, other
works point to the discriminator. We take a sober look at those explanations
and provide insights on what makes proposed measures against high-frequency
artifacts effective. To achieve this, we first independently assess the
architectures of both the generator and discriminator and investigate if they
exhibit a frequency bias that makes learning the distribution of high-frequency
content particularly problematic. Based on these experiments, we make the
following four observations: 1) Different upsampling operations bias the
generator towards different spectral properties. 2) Checkerboard artifacts
introduced by upsampling cannot explain the spectral discrepancies alone as the
generator is able to compensate for these artifacts. 3) The discriminator does
not struggle with detecting high frequencies per se but rather struggles with
frequencies of low magnitude. 4) The downsampling operations in the
discriminator can impair the quality of the training signal it provides. In
light of these findings, we analyze proposed measures against high-frequency
artifacts in state-of-the-art GAN training but find that none of the existing
approaches can fully resolve spectral artifacts yet. Our results suggest that
there is great potential in improving the discriminator and that this could be
key to match the distribution of the training data more closely.
