Projected GANs Converge Faster

Understand

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.