  Compositional Pattern Producing Networks (CPPNs) are differentiable networks
that independently map (x, y) pixel coordinates to (r, g, b) colour values.
Recently, CPPNs have been used for creating interesting imagery for creative
purposes, e.g., neural art. However their architecture biases generated images
to be overly smooth, lacking high-frequency detail. In this work, we extend
CPPNs to explicitly model the frequency information for each pixel output,
capturing frequencies beyond the DC component. We show that our Fourier-CPPNs
(F-CPPNs) provide improved visual detail for image synthesis.
