Omni-GAN: On the Secrets of cGANs and Beyond
arXiv:2011.13074
Abstract
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.
Introducing Omni-INR-GAN, which can extrapolate low-resolution images to arbitrary resolution
References in corpus (10)
- Conditional Generative Adversarial Nets
- GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
- Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
- A Learned Representation For Artistic Style
- Invertible Conditional GANs for image editing
- Adversarial Discriminative Domain Adaptation
- Dilated Residual Networks
- High-Fidelity Image Generation With Fewer Labels
- Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
- CircleGAN: Generative Adversarial Learning across Spherical Circles