CalvinBackup/awesome-gans-and-deepfakes
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Added DualGAN and ESRGAN

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Haswanth Aekula
2020-11-12 21:16:35 +05:30
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@@ -26,6 +26,7 @@ Tl;dr GANs containg two competing neural networks which iteratively generate new
+ :heavy_check_mark: CycleGAN: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, [[paper]](https://arxiv.org/abs/1703.10593), [[github]](https://github.com/junyanz/CycleGAN)
+ :heavy_check_mark: StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation, [[paper]](https://arxiv.org/abs/1711.09020), [[github]](https://github.com/yunjey/stargan)
+ :heavy_check_mark: Pix2Pix: Image-to-Image Translation with Conditional Adversarial Nets, [[paper]](https://arxiv.org/abs/1611.07004), [[github]](https://github.com/phillipi/pix2pix)
+ :heavy_check_mark: DualGAN: Unsupervised Dual Learning for Image-to-Image Translation, [[paper]](https://arxiv.org/abs/1704.02510), [[github]](https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/dualgan/dualgan.py)
### Volumetric (3D) Generation
+ :heavy_check_mark: 3DGAN: Learning a Probabilistic Latent Space of Object Shapes
@@ -52,6 +53,7 @@ via 3D Generative-Adversarial Modeling, [[paper]](http://3dgan.csail.mit.edu/pap
+ :heavy_check_mark: Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, [[paper]](https://arxiv.org/abs/1609.04802), [[github]](https://github.com/leehomyc/Photo-Realistic-Super-Resoluton)
+ High-Quality Face Image Super-Resolution Using Conditional Generative Adversarial Networks, [[paper]](https://arxiv.org/pdf/1707.00737.pdf)
+ :heavy_check_mark: Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network, [[paper]](https://arxiv.org/pdf/1811.00344.pdf), [[github]](https://github.com/subeeshvasu/2018_subeesh_epsr_eccvw)
+ :heavy_check_mark: ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks, [[paper]](https://arxiv.org/abs/1809.00219), [[github]](https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/esrgan/esrgan.py)
### Image Inpainting (hole filling)
+ :heavy_check_mark: Context Encoders: Feature Learning by Inpainting, [[paper]](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Pathak_Context_Encoders_Feature_CVPR_2016_paper.pdf), [[github]](https://github.com/pathak22/context-encoder)
@@ -103,4 +105,4 @@ Changing What You Want, [[paper]](http://vipl.ict.ac.cn/uploadfile/upload/201911
+ [Tampered Face (TAMFA) Dataset](https://www.sciencedirect.com/science/article/pii/S0957417419302350?via%3Dihub)
+ [Celeb-DF(v2) Celebrity Deepfake Dataset](http://www.cs.albany.edu/~lsw/celeb-deepfakeforensics.html)
+ [DeeperForensics-1.0](https://arxiv.org/pdf/2001.03024.pdf)
+ [Diverse Fake Face Dataset (DFFD)](https://arxiv.org/pdf/1910.01717.pdf)
+ [Diverse Fake Face Dataset (DFFD)](https://arxiv.org/pdf/1910.01717.pdf)