  The conditional generative adversarial network (cGAN) is a powerful tool of
generating high-quality images, but existing approaches mostly suffer
unsatisfying performance or the risk of mode collapse. This paper presents
Omni-GAN, a variant of cGAN that reveals the devil in designing a proper
discriminator for training the model. The key is to ensure that the
discriminator receives strong supervision to perceive the concepts and moderate
regularization to avoid collapse. Omni-GAN is easily implemented and freely
integrated with off-the-shelf encoding methods (e.g., implicit neural
representation, INR). Experiments validate the superior performance of Omni-GAN
and Omni-INR-GAN in a wide range of image generation and restoration tasks. In
particular, Omni-INR-GAN sets new records on the ImageNet dataset with
impressive Inception scores of 262.85 and 343.22 for the image sizes of 128 and
256, respectively, surpassing the previous records by 100+ points. Moreover,
leveraging the generator prior, Omni-INR-GAN can extrapolate low-resolution
images to arbitrary resolution, even up to x60+ higher resolution. Code is
available.
