From 428b0cd2184e8bca0c4f163f562b596288b852be Mon Sep 17 00:00:00 2001 From: Haswanth Aekula Date: Fri, 13 Nov 2020 13:06:15 +0530 Subject: [PATCH] Added CCGAN, BicycleGAN, MUNIT, SRGAN (#3) - `DualGAN` - Changed GitHub link to official implementation. - `ESRGAN` - Changed GitHub link to official implementation. - `CCGAN` - There is official implementation in `Lua`, so added `PyTorch` implementation. Let me know if you want to change it to official implementation. --- README.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 3e44db0..2236192 100644 --- a/README.md +++ b/README.md @@ -21,12 +21,14 @@ Tl;dr GANs containg two competing neural networks which iteratively generate new ### Conditional GANs + :heavy_check_mark: CGAN: Conditional Generative Adversarial Nets, [[paper]](https://arxiv.org/abs/1411.1784), [[github]](https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/cgan/cgan.py) + :heavy_check_mark: ACGAN: Conditional Image Synthesis With Auxiliary Classifier GANs, [[paper]](https://arxiv.org/abs/1610.09585), [[github]](https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/acgan/acgan.py) ++ :heavy_check_mark: CCGAN: Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks, [[paper]](https://arxiv.org/abs/1611.06430), [[github]](https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/ccgan/ccgan.py) ### Image-to-Image Translation + :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) ++ :heavy_check_mark: DualGAN: Unsupervised Dual Learning for Image-to-Image Translation, [[paper]](https://arxiv.org/abs/1704.02510), [[github]](https://github.com/duxingren14/DualGAN) ++ :heavy_check_mark: BicycleGAN: Toward Multimodal Image-to-Image Translation, [[paper]](https://arxiv.org/abs/1711.11586), [[github]](https://github.com/junyanz/BicycleGAN) ### Volumetric (3D) Generation + :heavy_check_mark: 3DGAN: Learning a Probabilistic Latent Space of Object Shapes @@ -53,7 +55,9 @@ 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) ++ :heavy_check_mark: ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks, [[paper]](https://arxiv.org/abs/1809.00219), [[github]](https://github.com/xinntao/ESRGAN) ++ :heavy_check_mark: MUNIT: Multimodal Unsupervised Image-to-Image Translation, [[paper]](https://arxiv.org/abs/1804.04732), [[github]](https://github.com/nvlabs/MUNIT) ++ :heavy_check_mark: SRGAN: Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, [[paper]](https://arxiv.org/abs/1609.04802), [[github]](https://github.com/eriklindernoren/PyTorch-GAN/blob/master/implementations/srgan/srgan.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)