{"id":45985,"url":"https://github.com/kozistr/Awesome-GANs","name":"Awesome-GANs","description":"Awesome Generative Adversarial Networks with tensorflow","projects_count":94,"last_synced_at":"2026-09-30T22:00:28.329Z","repository":{"id":37952669,"uuid":"92664599","full_name":"kozistr/Awesome-GANs","owner":"kozistr","description":"Awesome Generative Adversarial Networks with tensorflow","archived":false,"fork":false,"pushed_at":"2022-06-25T12:03:16.000Z","size":85551,"stargazers_count":757,"open_issues_count":12,"forks_count":164,"subscribers_count":26,"default_branch":"master","last_synced_at":"2026-09-11T01:06:32.803Z","etag":null,"topics":["acgan","arxiv","began","cgan","cogan","dcgan","dragan","ebgan","f-gan","gan","generative-adversarial-network","lapgan","lsgan","machine-learning","sagan","srgan","stargan","tensorflow","wgan","wgan-gp"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kozistr.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2017-05-28T14:00:08.000Z","updated_at":"2026-09-08T09:40:58.000Z","dependencies_parsed_at":"2022-09-12T17:00:19.618Z","dependency_job_id":null,"html_url":"https://github.com/kozistr/Awesome-GANs","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/kozistr/Awesome-GANs","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kozistr%2FAwesome-GANs","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kozistr%2FAwesome-GANs/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kozistr%2FAwesome-GANs/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kozistr%2FAwesome-GANs/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kozistr","download_url":"https://codeload.github.com/kozistr/Awesome-GANs/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kozistr%2FAwesome-GANs/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":342085742,"owners_count":37888762,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-08-22T15:14:58.755Z","status":"online","status_checked_at":"2026-09-30T02:00:06.001Z","response_time":133,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-01-14T01:26:45.065Z","updated_at":"2026-09-30T22:00:28.330Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Papers \u0026 Codes","Applied Others","Author","Useful Resources"],"sub_categories":["Theory \u0026 Concept","Applied Vision","Applied Audio","Start"],"readme":"# Awesome-GANs with Tensorflow\n\nTensorflow implementation of GANs (**Generative Adversarial Networks**)\n\n[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) \n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/kozistr/Awesome-GANs.svg?logo=lgtm\u0026logoWidth=18)](https://lgtm.com/projects/g/kozistr/Awesome-GANs/context:python)\n\n## **WIP** : This repo is about to be refactored \u0026 supporting `tf 2.x`.\n\nMaybe some codes wouldn't work on master branch, because i'm just working on the branch.\n\n## Environments\n\nBecause of the image and model size, (especially **BEGAN**, **SRGAN**, **StarGAN**, ... using high resolution images as input),\nif you want to train them comfortably, you need a GPU which has more than `8GB`.\n\nBut, of course, the most of the implementations use `MNIST` or `CIFAR-10, 100` DataSets.\nMeaning that we can handle it with EVEN lower spec GPU than 'The Preferred' :).\n\n## Usage\n\nNow on **refactoring**... All GAN training script can be run module-wisely like below. (**WIP**)\n\n### Install dependencies\n\nYou can also use *conda*, *virtualenv* environments.\n\n```shell script\n$ python3 -m pip install -r requirements.txt\n```\n\n### Train GANs\n\nBefore running the model, make sure that \n\n1. downloading the dataset like *CelebA*, *MNIST*, etc what you want\n2. In `awesome_gans/config.py`, there are several configurations, customize with your flavor!\n3. running the model like below\n\n```shell script\n$ python3 -m awesome_gans.acgan\n```\n\n## DataSets\n\nSupporting datasets are ... (code is in `/awesome_gans/datasets.py`)\n\n* MNIST / ~~Fashion MNIST~~\n* CIFAR10 / 100\n* CelebA/CelebA-HQ\n* Pix2Pix\n* DIV2K\n* (more DataSets will be added soon!)\n\n## Repo Tree\n\n```\n│\n├── awesome_gans (source codes \u0026 eplainations \u0026 results \u0026 models) \n│        │\n│        ├── acgan\n│        │    ├──gen_img (generated images)\n│        │    │     ├── train_xxx.png\n│        │    │     └── train_xxx.png\n│        │    ├── model  (pre-trained model file)\n│        │    │     └── model.txt (google-drive link)\n│        │    ├── __init__.py\n│        │    ├── __main__.py\n│        │    ├── model.py (gan model)\n│        │    ├── train.py (gan trainer)\n│        │    ├── gan_tb.png   (tensorboard loss plot)\n│        │    └── readme.md    (results \u0026 explainations)\n│        ├── config.py         (configurations)\n│        ├── modules.py        (networks \u0026 operations)\n│        ├── utils.py          (auxiliary utils)\n│        ├── image_utils.py    (image processing)\n│        └── datasets.py       (dataset loader)\n├── CONTRIBUTING.md\n├── Makefile   (for linting the codes)\n├── LICENSE\n├── README.md  (Usage \u0026 GAN paper list-up)\n└── requirements.txt\n```\n\n## Papers \u0026 Codes\n\nHere's the list-up for tons of GAN papers. all papers are sorted by alphabetic order.\n\n### Start\n\nHere's the beginning of the **GAN**.\n\n| *Name*  |             *Summary*             |                  *Paper*                   |            *Code*            |\n|:-------:|:---------------------------------:|:------------------------------------------:|:----------------------------:|\n| **GAN** | *Generative Adversarial Networks* | [[arXiv]](https://arxiv.org/abs/1406.2661) | [[code]](./awesome_gans/GAN) |\n\n### Theory \u0026 Concept\n\nHere for the theories \u0026 concepts of the GAN.\n\n|      *Name*       |                                             *Summary*                                             |                                                                             *Paper*                                                                             |                                 *Code*                                 |                           *Official Code*                            |\n|:-----------------:|:-------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------:|:--------------------------------------------------------------------:|\n|     **ACGAN**     |                      *Auxiliary Classifier Generative Adversarial Networks*                       |                                                           [[arXiv]](https://arxiv.org/abs/1610.09585)                                                           |                     [[code]](./awesome_gans/ACGAN)                     |\n|    **AdaGAN**     |                                   *Boosting Generative Models*                                    |                                                           [[arXiv]](https://arxiv.org/abs/1701.02386)                                                           |                             [[~~code~~]]()                             |\n|      **bCR**      |                          *Improved Consistency Regularization for GANs*                           |                                                           [[arXiv]](https://arxiv.org/abs/2002.04724)                                                           |                             [[~~code~~]]()                             |\n|     **BEGAN**     |                      *Boundary Equilibrium Generative Adversarial Networks*                       |                                                           [[arXiv]](https://arxiv.org/abs/1703.10717)                                                           |                     [[code]](./awesome_gans/BEGAN)                     |\n|     **BGAN**      |                        *Boundary-Seeking Generative Adversarial Networks*                         |                                                           [[arXiv]](https://arxiv.org/abs/1702.08431)                                                           |                     [[code]](./awesome_gans/BGAN)                      |\n|    **BigGAN**     |               *Large Scale GAN Training for High Fidelity Natural Image Synthesis*                |                                                           [[arXiv]](https://arxiv.org/abs/1809.11096)                                                           |                             [[~~code~~]]()                             |\n|     **CGAN**      |                           *Conditional Generative Adversarial Networks*                           |                                                           [[arXiv]](https://arxiv.org/abs/1411.1784)                                                            |                     [[code]](./awesome_gans/CGAN)                      |\n|     **CoGAN**     |                             *Coupled Generative Adversarial Networks*                             |                                                           [[arXiv]](https://arxiv.org/abs/1606.07536)                                                           |                     [[code]](./awesome_gans/CoGAN)                     |\n|   **ConSinGAN**   |                       *Improved Techniques for Training Single-Image GANs*                        |          [[WACV21]](https://openaccess.thecvf.com/content/WACV2021/papers/Hinz_Improved_Techniques_for_Training_Single-Image_GANs_WACV_2021_paper.pdf)          |                             [[~~code~~]]()                             |          [[official]](https://github.com/tohinz/ConSinGAN)           |\n|     **DCGAN**     |                       *Deep Convolutional Generative Adversarial Networks*                        |                                                           [[arXiv]](https://arxiv.org/abs/1511.06434)                                                           |                     [[code]](./awesome_gans/DCGAN)                     |\n|    **DRAGAN**     |                 *On Convergence and Stability of Generative Adversarial Networks*                 |                                                           [[arXiv]](https://arxiv.org/abs/1705.07215)                                                           |                    [[code]](./awesome_gans/DRAGAN)                     |\n|     **EBGAN**     |                          *Energy-based Generative Adversarial Networks*                           |                                                           [[arXiv]](https://arxiv.org/abs/1609.03126)                                                           |                     [[code]](./awesome_gans/EBGAN)                     |\n|   **EqGAN-SA**    |                     *Improving GAN Equilibrium by Raising Spatial Awareness*                      |                                                         [[arXiv]](https://arxiv.org/pdf/2112.00718.pdf)                                                         |                             [[~~code~~]]()                             |          [[official]](https://github.com/genforce/eqgan-sa)          |\n|     **f-GAN**     |          *Training Generative Neural Samplers using Variational Divergence Minimization*          |                                                           [[arXiv]](https://arxiv.org/abs/1606.00709)                                                           |                     [[code]](./awesome_gans/FGAN)                      |\n|    **GP-GAN**     |                        *Towards Realistic High-Resolution Image Blending*                         |                                                           [[arXiv]](https://arxiv.org/abs/1703.07195)                                                           |                             [[~~code~~]]()                             |\n|  **Softmax GAN**  |                          *Generative Adversarial Networks with Softmax*                           |                                                           [[arXiv]](https://arxiv.org/abs/1704.06191)                                                           |                      [[code]](./awesome_gans/GAN)                      |\n|      **GAP**      |                             *Generative Adversarial Parallelization*                              |                                                           [[arXiv]](https://arxiv.org/abs/1612.04021)                                                           |                             [[~~code~~]]()                             |\n|     **GEGAN**     |                *Generalization and Equilibrium in Generative Adversarial Networks*                |                                                           [[arXiv]](https://arxiv.org/abs/1703.00573)                                                           |                             [[~~code~~]]()                             |\n|     **G-GAN**     |                                          *Geometric GAN*                                          |                                                           [[arXiv]](https://arxiv.org/abs/1705.02894)                                                           |                             [[~~code~~]]()                             | \n|    **InfoGAN**    | *Interpretable Representation Learning by Information Maximizing Generative Adversarial Networks* |                                                           [[arXiv]](https://arxiv.org/abs/1606.03657)                                                           |                    [[code]](./awesome_gans/InfoGAN)                    |\n|    **LAPGAN**     |                        *Laplacian Pyramid Generative Adversarial Networks*                        |                                                           [[arXiv]](https://arxiv.org/abs/1506.05751)                                                           |                    [[code]](./awesome_gans/LAPGAN)                     |\n|     **LSGAN**     |                         *Loss-Sensitive Generative Adversarial Networks*                          |                                                           [[arXiv]](https://arxiv.org/abs/1701.06264)                                                           |                     [[code]](./awesome_gans/LSGAN)                     |\n|     **MAGAN**     |                      *Margin Adaptation for Generative Adversarial Networks*                      |                                                           [[arXiv]](https://arxiv.org/abs/1704.03817)                                                           |                     [[code]](./awesome_gans/MAGAN)                     |\n|     **MRGAN**     |                        *Mode Regularized Generative Adversarial Networks*                         |                                                           [[arXiv]](https://arxiv.org/abs/1612.02136)                                                           |                     [[code]](./awesome_gans/MRGAN)                     |\n|    **MSGGAN**     |                    *Multi-Scale Gradients for Generative Adversarial Networks*                    |                                                           [[arXiv]](https://arxiv.org/abs/1903.06048)                                                           |                             [[~~code~~]]()                             |\n|     **PGGAN**     |           *Progressive Growing of GANs for Improved Quality, Stability, and Variation*            |                                                           [[arXiv]](https://arxiv.org/abs/1710.10196)                                                           |                             [[~~code~~]]()                             | [[official]](https://github.com/tkarras/progressive_growing_of_gans) |\n|     **RaGAN**     |             *The relativistic discriminator: a key element missing from standard GAN*             |                                                        [[arXiv]](https://arxiv.org/pdf/1807.00734v3.pdf)                                                        |                             [[~~code~~]]()                             |\n|    **SeAtGAN**    |                         *Self-Attention Generative Adversarial Networks*                          |                                                           [[arXiv]](https://arxiv.org/abs/1805.08318)                                                           |                     [[code]](./awesome_gans/SAGAN)                     |\n|   **SphereGAN**   |            *Sphere Generative Adversarial Network Based on Geometric Moment Matching*             |                                               [[CVPR2019]](http://cau.ac.kr/~jskwon/paper/SphereGAN_CVPR2019.pdf)                                               |                             [[~~code~~]]()                             |\n|     **SGAN**      |                             *Stacked Generative Adversarial Networks*                             |                                                           [[arXiv]](https://arxiv.org/abs/1612.04357)                                                           | [[~~code~~]](https://github.com/kozistr/Awesome-GANs/blob/master/SGAN) |\n|    **SGAN++**     |             *Realistic Image Synthesis with Stacked Generative Adversarial Networks*              |                                                           [[arXiv]](https://arxiv.org/abs/1710.10916)                                                           | [[~~code~~]](https://github.com/kozistr/Awesome-GANs/blob/master/SGAN) |\n|    **SinGAN**     |                     *Learning a Generative Model from a Single Natural Image*                     |                                                           [[arXiv]](https://arxiv.org/abs/1905.01164)                                                           |                             [[~~code~~]]()                             |           [[official]](https://github.com/tamarott/SinGAN)           |\n|   **StableGAN**   |                      *Stabilizing Adversarial Nets With Prediction Methods*                       |                                                           [[arXiv]](https://arxiv.org/abs/1705.07364)                                                           |                             [[~~code~~]]()                             |\n|   **StyleCLIP**   |                          *Text-Driven Manipulation of StyleGAN Imagery*                           |                                                           [[arXiv]](https://arxiv.org/abs/2103.17249)                                                           |                             [[~~code~~]]()                             |        [[official]](https://github.com/orpatashnik/StyleCLIP)        | \n|   **StyleGAN**    |            *A Style-Based Generator Architecture for Generative Adversarial Networks*             |                                                           [[arXiv]](https://arxiv.org/abs/1812.04948)                                                           |                             [[~~code~~]]()                             |           [[official]](https://github.com/NVlabs/stylegan)           |\n|   **StyleGAN2**   |                      *Analyzing and Improving the Image Quality of StyleGAN*                      |                                                           [[arXiv]](http://arxiv.org/abs/1912.04958)                                                            |                             [[~~code~~]]()                             |          [[official]](https://github.com/NVlabs/stylegan2)           |\n| **StyleGAN2 ADA** |                       *StyleGAN2 with adaptive discriminator augmentation*                        |                                                           [[arXiv]](https://arxiv.org/abs/2006.06676)                                                           |                             [[~~code~~]]()                             |        [[official]](https://github.com/NVlabs/stylegan2-ada)         |\n|   **StyleGAN3**   |                           *Alias-Free Generative Adversarial Networks*                            |                                                           [[arXiv]](https://arxiv.org/abs/2106.12423)                                                           |                             [[~~code~~]]()                             |          [[official]](https://github.com/NVlabs/stylegan3)           |\n|  **StyleGAN-XL**  |                           *Scaling StyleGAN to Large Diverse Datasets*                            |                                                           [[arXiv]](https://arxiv.org/abs/2202.00273)                                                           |                             [[~~code~~]]()                             |    [[official]](https://github.com/autonomousvision/stylegan_xl)     |\n|   **TripleGAN**   |                             *Triple Generative Adversarial Networks*                              |                                                           [[arXiv]](https://arxiv.org/abs/1703.02291)                                                           |                             [[~~code~~]]()                             |\n|     **UGAN**      |                            *Unrolled Generative Adversarial Networks*                             |                                                           [[arXiv]](https://arxiv.org/abs/1611.02163)                                                           |                             [[~~code~~]]()                             |\n|   **U-Net GAN**   |                 *A U-Net Based Discriminator for Generative Adversarial Networks*                 | [[CVPR20]](https://openaccess.thecvf.com/content_CVPR_2020/html/Schonfeld_A_U-Net_Based_Discriminator_for_Generative_Adversarial_Networks_CVPR_2020_paper.html) |                             [[~~code~~]]()                             |        [[official]](https://github.com/boschresearch/unetgan)        | \n|     **WGAN**      |                           *Wasserstein Generative Adversarial Networks*                           |                                                           [[arXiv]](https://arxiv.org/abs/1701.07875)                                                           |                     [[code]](./awesome_gans/WGAN)                      |\n|    **WGAN-GP**    |                *Improved Training of Wasserstein Generative Adversarial Networks*                 |                                                           [[arXiv]](https://arxiv.org/abs/1704.00028)                                                           |                     [[code]](./awesome_gans/WGAN)                      |\n\n### Applied Vision\n\nHere for the GAN applications on Vision domain, \nlike image-to-image translation, image in-painting, single image super resolution , etc.\n\n|     *Name*      |                                                 *Summary*                                     |                                                                              *Paper*                                                                            |                *Code*             |                              *Official Code*                    |\n|:---------------:|:---------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------:|:---------------------------------------------------------------:|\n|   **3D GAN**    |                             *3D Generative Adversarial Networks*                              |                                                              [[MIT]](http://3dgan.csail.mit.edu/)                                                               |          [[~~code~~]]()           |\n| **AnycostGAN**  |                  *Anycost GANs for Interactive Image Synthesis and Editing*                   |                                                           [[arXiv]](https://arxiv.org/abs/2103.03243)                                                           |          [[~~code~~]]()           |    [[official]](https://github.com/mit-han-lab/anycost-gan)     |\n|  **CycleGAN**   |          *Unpaired img2img translation using Cycle-consistent Adversarial Networks*           |                                                           [[arXiv]](https://arxiv.org/abs/1703.10593)                                                           | [[code]](./awesome_gans/CycleGAN) |\n|    **DAGAN**    |     *Instance-level Image Translation by Deep Attention Generative Adversarial Networks*      |                                                           [[arXiv]](https://arxiv.org/abs/1802.06454)                                                           |          [[~~code~~]]()           |\n|  **DeblurGAN**  |               *Blind Motion Deblurring Using Conditional Adversarial Networks*                |                                                           [[arXiv]](https://arxiv.org/abs/1711.07064)                                                           |          [[~~code~~]]()           |\n|   **DualGAN**   |                  *Unsupervised Dual Learning for Image-to-Image Translation*                  |                                                           [[arXiv]](https://arxiv.org/abs/1704.02510)                                                           |          [[~~code~~]]()           |\n|   **DRIT/++**   |             *Diverse Image-to-Image Translation via Disentangled Representations*             |                                                           [[arXiv]](https://arxiv.org/abs/1905.01270)                                                           |          [[~~code~~]]()           |        [[official]](https://github.com/HsinYingLee/DRIT)        |\n| **EdgeConnect** |                 *Generative Image Inpainting with Adversarial Edge Learning*                  |                                                           [[arXiv]](https://arxiv.org/abs/1901.00212)                                                           |          [[~~code~~]]()           |      [[official]](https://github.com/knazeri/edge-connect)      |\n|   **ESRGAN**    |                  *Enhanced Super-Resolution Generative Adversarial Networks*                  |                                                           [[arXiv]](https://arxiv.org/abs/1809.00219)                                                           |          [[~~code~~]]()           |\n|   **FastGAN**   |    *Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis*    |                                                           [[arXiv]](https://arxiv.org/abs/2101.04775)                                                           |          [[~~code~~]]()           |  [[official]](https://github.com/odegeasslbc/FastGAN-pytorch)   |\n|    **FUNIT**    |                      *Few-Shot Unsupervised Image-to-Image Translation*                       |                                                           [[arXiv]](https://arxiv.org/abs/1905.01723)                                                           |          [[~~code~~]]()           |          [[official]](https://github.com/NVlabs/FUNIT)          |\n|   **CA \u0026 GA**   |           *Generative Image Inpainting w/ Contextual Attention \u0026 Gated Convolution*           |                                 [[CVPR2018]](https://arxiv.org/abs/1801.07892), [[ICCV2019]](https://arxiv.org/abs/1806.03589)                                  |          [[~~code~~]]()           | [[official]](https://github.com/JiahuiYu/generative_inpainting) |\n|  **HiFaceGAN**  |               *Face Renovation via Collaborative Suppression and Replenishment*               |                                                          [[arXiv]](https://arxiv.org/abs/2005.05005v1)                                                          |          [[~~code~~]]()           |\n|    **MUNIT**    |                     *Multimodal Unsupervised Image-to-Image Translation*                      |                                                           [[arXiv]](https://arxiv.org/abs/1804.04732)                                                           |          [[~~code~~]]()           |          [[official]](https://github.com/NVlabs/MUNIT)          |\n|  **NICE-GAN**   |                             *Reusing Discriminators for Encoding*                             |                                                           [[arXiv]](https://arxiv.org/abs/2003.00273)                                                           |          [[~~code~~]]()           |    [[official]](https://github.com/alpc91/NICE-GAN-pytorch)     |\n|    **PSGAN**    |        *Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer*        |                                                           [[arXiv]](https://arxiv.org/abs/1909.06956)                                                           |          [[~~code~~]]()           |        [[official]](https://github.com/wtjiang98/PSGAN)         |\n|   **SpAtGAN**   |      *Generative Adversarial Network with Spatial Attention for Face Attribute Editing*       |                [[ECCV2018]](http://openaccess.thecvf.com/content_ECCV_2018/html/Gang_Zhang_Generative_Adversarial_Network_ECCV_2018_paper.html)                 |          [[~~code~~]]()           |\n|   **SalGAN**    |                 *Visual Saliency Prediction Generative Adversarial Networks*                  |                                                           [[arXiv]](https://arxiv.org/abs/1701.01081)                                                           |          [[~~code~~]]()           |\n|   **SRFlow**    |                           *Super-Resolution using Normalizing Flow*                           |                                                           [[arXiv]](https://arxiv.org/abs/2006.14200)                                                           |          [[~~code~~]]()           |       [[official]](https://github.com/andreas128/SRFlow)        |\n|    **SRGAN**    |    *Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network*     |                                                           [[arXiv]](https://arxiv.org/abs/1609.04802)                                                           |  [[code]](./awesome_gans/SRGAN)   |\n|  **SRResCGAN**  | *Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-Resolution* |                                                           [[arXiv]](https://arxiv.org/abs/2005.00953)                                                           |          [[~~code~~]]()           |       [[official]](https://github.com/RaoUmer/SRResCGAN)        |\n|   **StarGAN**   |     *Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation*     |                                                           [[arXiv]](https://arxiv.org/abs/1711.09020)                                                           | [[code]](./awesome_gans/StarGAN)  |         [[official]](https://github.com/yunjey/stargan)         |\n| **StarGAN V2**  |                        *Diverse Image Synthesis for Multiple Domains*                         |                                                           [[arXiv]](https://arxiv.org/abs/1912.01865)                                                           |          [[~~code~~]]()           |       [[official]](https://github.com/clovaai/stargan-v2)       |\n| **StyleGAN-V**  |      *A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2*      |                                         [[arXiv]](https://kaust-cair.s3.amazonaws.com/stylegan-v/stylegan-v-paper.pdf)                                          |          [[~~code~~]]()           |     [[official]](https://github.com/universome/stylegan-v)      |\n|   **TecoGAN**   |       *Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation*       |                                                           [[arXiv]](https://arxiv.org/abs/1811.09393)                                                           |          [[~~code~~]]()           |         [[official]](https://github.com/thunil/TecoGAN)         |\n| **TextureGAN**  |                    *Controlling Deep Image Synthesis with Texture Patches*                    |                                                           [[arXiv]](https://arxiv.org/abs/1706.02823)                                                           |          [[~~code~~]]()           |\n|    **TUNIT**    |                *Rethinking the Truly Unsupervised Image-to-Image Translation*                 |                                                           [[arXiv]](https://arxiv.org/abs/2006.06500)                                                           |          [[~~code~~]]()           |         [[official]](https://github.com/clovaai/tunit)          |\n|   **TwinGAN**   |                         *Cross-Domain Translation fo Human Portraits*                         |                                                        [[github]](https://github.com/jerryli27/TwinGAN)                                                         |          [[~~code~~]]()           |\n|    **UNIT**     |                      *Unsupervised Image-to-Image Translation Networks*                       |                                                           [[arXiv]](https://arxiv.org/abs/1703.00848)                                                           |          [[~~code~~]]()           |        [[official]](https://github.com/mingyuliutw/UNIT)        |\n|    **XGAN**     |              *Unsupervised Image-to-Image Translation for Many-to-Many Mappings*              |                                                           [[arXiv]](https://arxiv.org/abs/1711.05139)                                                           |          [[~~code~~]]()           |\n|  **Zero-DCE**   |            *Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement*             | [[CVPR20]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Guo_Zero-Reference_Deep_Curve_Estimation_for_Low-Light_Image_Enhancement_CVPR_2020_paper.pdf) |          [[~~code~~]]()           |      [[official]](https://github.com/Li-Chongyi/Zero-DCE)       |\n\n### Applied Audio\n\nHere for the GAN applications on Audio domain, \nlike wave generation, wave to wave translation, etc.\n\n|        *Name*        |                                       *Summary*                                        |                           *Paper*                            |     *Code*     | *Official Code* |\n|:--------------------:|:--------------------------------------------------------------------------------------:|:------------------------------------------------------------:|:--------------:| :---: |\n|       **AAS**        |                             *Adversarial Audio Synthesis*                              |           [[arXiv]](https://arxiv.org/abs/1802.04208)        | [[~~code~~]]() | |\n|     **BeatGAN**      |                            *Generating Drum Loops via GANs*                            | [[arXiv]](https://github.com/NarainKrishnamurthy/BeatGAN2.0) | [[~~code~~]]() | |\n|     **GANSynth**     |                          *Adversarial Neural Audio Synthesis*                          |         [[arXiv]](https://arxiv.org/abs/1902.08710)          | [[~~code~~]]() | |\n|     **MuseGAN**      |     *Multi-track Sequential GANs for Symbolic Music Generation and Accompaniment*      |         [[arXiv]](https://arxiv.org/abs/1709.06298)          | [[~~code~~]]() | |\n|      **SEGAN**       |                  *Speech Enhancement Generative Adversarial Network*                   |         [[arXiv]](https://arxiv.org/abs/1703.09452)          | [[~~code~~]]() | |\n|    **StarGAN-VC**    | *Non-parallel many-to-many voice conversion with star generative adversarial networks* |         [[arXiv]](https://arxiv.org/abs/1806.02169)          | [[~~code~~]]() | |\n|     **TempoGAN**     |        *A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow*         |         [[arXiv]](https://arxiv.org/abs/1801.09710)          | [[~~code~~]]() | |\n| **Parallel WaveGAN** |   *A fast waveform generation model based on GAN with multi-resolution spectrogram*    |         [[arXiv]](https://arxiv.org/abs/1910.11480)          | [[~~code~~]]() | |\n|     **WaveGAN**      |               *Synthesizing Audio with Generative Adversarial Networks*                |         [[arXiv]](https://arxiv.org/abs/1802.04208)          | [[~~code~~]]() | |\n\n## Applied Others\n\nHere for the GAN applications on other domains, \nlike nlp, tabular, etc.\n\n|      *Name*      |                                 *Summary*                              |                     *Paper*                     |         *Code*         | *Official Code* |\n|:----------------:|:----------------------------------------------------------------------:|:-----------------------------------------------:|:----------------------:|:---------------:|\n|   **AnoGAN**     | *Unsupervised Anomaly Detection with Generative Adversarial Networks*  |   [[arXiv]](https://arxiv.org/abs/1703.05921)   | [[~~code~~]](./AnoGAN) |\n|  **CipherGAN**   |           *Unsupervised Cipher Cracking Using Discrete GANs*           |  [[github]](https://arxiv.org/abs/1801.04883)   |     [[~~code~~]]()     |\n|   **DiscoGAN**   |        *Discover Cross-Domain Generative Adversarial Networks*         |   [[arXiv]](https://arxiv.org/abs/1703.05192)   |     [[~~code~~]]()     |\n| **eCommerceGAN** |           *A Generative Adversarial Network for E-commerce*            |   [[arXiv]](https://arxiv.org/abs/1801.03244)   |     [[~~code~~]]()     |\n|   **PassGAN**    |            *A Deep Learning Approach for Password Guessing*            |   [[arXiv]](https://arxiv.org/abs/1709.00440)   |     [[~~code~~]]()     |\n|    **SeqGAN**    |    *Sequence Generative Adversarial Networks with Policy Gradient*     |   [[arXiv]](https://arxiv.org/abs/1609.05473)   |     [[~~code~~]]()     |\n|   **TAC-GAN**    | *Text Conditioned Auxiliary Classifier Generative Adversarial Network* | [[arXiv]](https://arxiv.org/abs/1703.06412.pdf) |     [[~~code~~]]()     |\n\n## Useful Resources\n\nHere for the useful resources when you try to train and stable a gan model.\n\n|  *Name*   |                  *Summary*                   |                    *Link*                     |\n|:---------:|:--------------------------------------------:|:---------------------------------------------:|\n| GAN Hacks | a bunch of tips \u0026 tricks to train GAN stable | [github](https://github.com/soumith/ganhacks) |\n\n## Note\n\nAny suggestions and PRs and issues are WELCOME :)\n\n## Author\n\nHyeongChan Kim / [@kozistr](http://kozistr.tech)\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/kozistr%2Fawesome-gans/projects"}