Image Generation
Image Generation (synthesis) is the task of generating new images from an existing dataset.
- Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
- Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.
In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.
( Image credit: StyleGAN )
Papers
Showing 1–10 of 6689 papers
All datasetsImageNet 256x256CIFAR-10ImageNet 64x64ImageNet 512x512FFHQ 256 x 256CelebA 64x64ImageNet 32x32LSUN Bedroom 256 x 256STL-10LSUN Churches 256 x 256ImageNet 128x128FFHQ 1024 x 1024
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | StyleALAE | FID | 13.09 | — | Unverified |
| 2 | CIPS | FID | 10.07 | — | Unverified |
| 3 | HiT-B | FID | 6.37 | — | Unverified |
| 4 | MSG-StyleGAN | FID | 5.8 | — | Unverified |
| 5 | StyleSwin | FID | 5.07 | — | Unverified |
| 6 | StyleGAN | FID | 4.4 | — | Unverified |
| 7 | StyleNAT | FID | 4.17 | — | Unverified |
| 8 | SWAGAN-Bi | FID | 4.06 | — | Unverified |
| 9 | StyleGAN2 ADA+bCR | FID | 3.62 | — | Unverified |
| 10 | FQ-GAN | FID | 3.19 | — | Unverified |