  Generative Adversarial Networks (GANs) produce high-quality images but are
challenging to train. They need careful regularization, vast amounts of
compute, and expensive hyper-parameter sweeps. We make significant headway on
these issues by projecting generated and real samples into a fixed, pretrained
feature space. Motivated by the finding that the discriminator cannot fully
exploit features from deeper layers of the pretrained model, we propose a more
effective strategy that mixes features across channels and resolutions. Our
Projected GAN improves image quality, sample efficiency, and convergence speed.
It is further compatible with resolutions of up to one Megapixel and advances
the state-of-the-art Fr\'echet Inception Distance (FID) on twenty-two benchmark
datasets. Importantly, Projected GANs match the previously lowest FIDs up to 40
times faster, cutting the wall-clock time from 5 days to less than 3 hours
given the same computational resources.
