  FREDSR is a GAN variant that aims to outperform traditional GAN models in
specific tasks such as Single Image Super Resolution with extreme parameter
efficiency at the cost of per-dataset generalizeability. FREDSR integrates fast
Fourier transformation, residual prediction, diffusive discriminators, etc to
achieve strong performance in comparisons to other models on the UHDSR4K
dataset for Single Image 3x Super Resolution from 360p and 720p with only 37000
parameters. The model follows the characteristics of the given dataset,
resulting in lower generalizeability but higher performance on tasks such as
real time up-scaling.
