# gans-awesome-applications
**A curated list of awesome GAN _applications_ and demonstrations.**
[](https://awesome.re)
[](LICENSE)
[](CONTRIBUTING.md)
> **Only awesome is awesome. This is a curation, not a collection.**
>
> This list is about what GANs are *used for*. General GAN papers whose contribution is the model
> itself (DCGAN, BEGAN, WGAN, โฆ) are **not** included as entries โ only genuine landmarks make the
> short list at the top. Pure theory / loss-function papers are out of scope; diffusion-only work is
> out of scope (GAN+diffusion hybrids are fine).
๐ท **Legend:** ๐ paper ยท ๐ป code ยท ๐ project/demo ยท ๐ฌ video ยท ๐ blog
๐ค Contributions welcome โ see **[CONTRIBUTING.md](CONTRIBUTING.md)**.
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## ๐ The landmark papers that I respect
| Paper | Venue | Year | Links |
|:--|:--:|:--:|:--|
| Generative Adversarial Networks | NeurIPS | 2014 | [๐](https://arxiv.org/abs/1406.2661) [๐ป](https://github.com/goodfeli/adversarial) |
| Unsupervised Representation Learning with Deep Convolutional GANs (DCGAN) | ICLR | 2016 | [๐](https://arxiv.org/pdf/1511.06434) [๐ป](https://github.com/soumith/dcgan.torch) |
| Improved Techniques for Training GANs | NeurIPS | 2016 | [๐](https://arxiv.org/pdf/1606.03498.pdf) [๐ป](https://github.com/openai/improved-gan) |
| BEGAN: Boundary Equilibrium Generative Adversarial Networks | โ | 2017 | [๐](https://arxiv.org/pdf/1703.10717) [๐ป](https://github.com/carpedm20/BEGAN-tensorflow) |
| Training Generative Adversarial Networks with Limited Data (StyleGAN2-ADA) | NeurIPS | 2020 | [๐](https://arxiv.org/abs/2006.06676) [๐ป](https://github.com/NVlabs/stylegan2-ada-pytorch) |
| The GAN is dead; long live the GAN! A Modern GAN Baseline (R3GAN) | NeurIPS | 2024 | [๐](https://arxiv.org/abs/2501.05441) [๐ป](https://github.com/brownvc/R3GAN) |
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## Contents