Papers › Omni-GAN: On the Secrets of cGANs and Beyond
Omni-GAN: On the Secrets of cGANs and Beyond
Peng Zhou, Lingxi Xie, Bingbing Ni, Cong Geng, Qi Tian
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.
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Code
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Conditional Image Generation | ImageNet 128x128 | Omni-INR-GAN | FID | 6.53 | #8 of 22 | Archive leaderboard | report |
| Conditional Image Generation | ImageNet 128x128 | Omni-INR-GAN | Inception score | 262.85 | #8 of 22 | Archive leaderboard | report |
| Conditional Image Generation | ImageNet 128x128 | Omni-GAN | FID | 8.30 | #13 of 22 | Archive leaderboard | report |
| Conditional Image Generation | ImageNet 128x128 | Omni-GAN | Inception score | 190.94 | #13 of 22 | Archive leaderboard | report |
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