{"id":18551343,"url":"https://github.com/morvanzhou/mnistgans","last_synced_at":"2025-04-09T18:19:03.760Z","repository":{"id":41321578,"uuid":"304512574","full_name":"MorvanZhou/mnistGANs","owner":"MorvanZhou","description":"Some implementations of GAN.","archived":false,"fork":false,"pushed_at":"2023-10-26T08:27:23.000Z","size":206,"stargazers_count":217,"open_issues_count":1,"forks_count":58,"subscribers_count":4,"default_branch":"main","last_synced_at":"2025-04-09T18:18:57.151Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://mofanpy.com","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/MorvanZhou.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-10-16T03:45:53.000Z","updated_at":"2025-03-27T13:05:54.000Z","dependencies_parsed_at":"2024-12-18T01:09:50.337Z","dependency_job_id":"9ad64e10-8c00-4650-907f-411636520d2c","html_url":"https://github.com/MorvanZhou/mnistGANs","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MorvanZhou%2FmnistGANs","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MorvanZhou%2FmnistGANs/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MorvanZhou%2FmnistGANs/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MorvanZhou%2FmnistGANs/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/MorvanZhou","download_url":"https://codeload.github.com/MorvanZhou/mnistGANs/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248085325,"owners_count":21045139,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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"}},"keywords":[],"created_at":"2024-11-06T21:08:44.407Z","updated_at":"2025-04-09T18:19:03.735Z","avatar_url":"https://github.com/MorvanZhou.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# GANs implementation using MNIST data\n\nThis repo is a collection of the implementations of many GANs. \nIn order to make the codes easy to read and follow,\n I minimize the code and run on the same MNIST dataset.\n\nWhat does the MNIST data look like?\n\n![](https://mofanpy.com/static/results/gan/mnist.png)\n\nToy implementations are organized as following:\n\n**1. Base Method**\n- [GAN](#GAN)\n- [Deep Convolutional GAN (DCGAN)](#DCGAN) \n\n**2. Loss or Structure Modifications**\n\n- [Least Squares GAN (LSGAN)](#LSGAN)\n- [Wasserstein GAN (WGAN)](#WGAN) \n    - [Gradient Penalty (WGAN gp)](#WGANpg) \n    - [Wasserstein Divergence (WGAN_div)](#WGANdiv)\n- [Self-Attention GAN (SAGAN)](#SAGAN)\n- [Progressive-Growing GAN (PGGAN)](#PGGAN)\n    \n**3. Can be Conditional**\n\n- [Conditional GAN (CGAN)](#CGAN) \n- [Auxiliary Classifier GAN (ACGAN)](#ACGAN) \n- [InfoGAN](#InfoGAN) \n- [StyleGAN](#StyleGAN)\n\n**4. Image to Image Transformation**\n\n- [Context-Conditional GAN (CCGAN)](#CCGAN)\n- [Pix2Pix](#Pix2Pix)\n- [CycleGAN](#CycleGAN)\n- [Super-Resolution GAN (SRGAN)](#SRGAN)\n- WIP\n \n# Installation\n```shell script\n$ git clone https://github.com/MorvanZhou/mnistGANs\n$ cd mnistGANs/\n$ pip3 install -r requirements.txt\n```\n\n \n## GAN\n[Generative Adversarial Nets](https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf)\n\n[code](gan.py) - [gif result](https://mofanpy.com/static/results/gan/gan/generating.gif)\n\n![](https://mofanpy.com/static/results/gan/gan/generating.gif)\n\n## DCGAN\n[Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/pdf/1511.06434.pdf)\n\n[code](dcgan.py) - [gif result](https://mofanpy.com/static/results/gan/dcgan/generating.gif)\n\n![](https://mofanpy.com/static/results/gan/dcgan/generating.gif)\n \n## LSGAN\n[Least Squares Generative Adversarial Networks](https://arxiv.org/pdf/1611.04076.pdf)\n\n[code](lsgan.py) - [gif result](https://mofanpy.com/static/results/gan/lsgan/generating.gif)\n\n![](https://mofanpy.com/static/results/gan/lsgan/generating.gif)\n\n## WGAN\n[Wasserstein GAN](https://arxiv.org/pdf/1701.07875.pdf)\n\n[code](wgan.py) - [gif result](https://mofanpy.com/static/results/gan/wgan/generating.gif)\n\n![](https://mofanpy.com/static/results/gan/wgan/generating.gif)\n\n## WGANpg\n[Improved Training of Wasserstein GANs](https://arxiv.org/pdf/1704.00028.pdf)\n\n[code](wgan_gp.py) - [gif result](https://mofanpy.com/static/results/gan/wgangp/generating.gif)\n\n![](https://mofanpy.com/static/results/gan/wgangp/generating.gif)\n\n## WGANdiv\n[Wasserstein Divergence for GANs](https://arxiv.org/pdf/1712.01026.pdf)\n\n[code](wgan_div.py) - [gif result](https://mofanpy.com/static/results/gan/wgandiv/generating.gif)\n\n![](https://mofanpy.com/static/results/gan/wgandiv/generating.gif)\n\n## SAGAN\n[Self-Attention Generative Adversarial Networks](https://arxiv.org/pdf/1805.08318.pdf)\n\n[code](sagan.py) - [gif result](https://mofanpy.com/static/results/gan/sagan/generating.gif)\n\n![](https://mofanpy.com/static/results/gan/sagan/generating.gif)\n\n## PGGAN\n[PROGRESSIVE GROWING OF GANS FOR IMPROVED QUALITY, STABILITY, AND VARIATION](https://arxiv.org/pdf/1710.10196.pdf)\n\n[code](pggan.py) - [gif result](https://mofanpy.com/static/results/gan/pggan/generating.gif)\n\n![](https://mofanpy.com/static/results/gan/pggan/generating.gif)\n\n## CGAN\n[Conditional Generative Adversarial Nets](https://arxiv.org/pdf/1411.1784.pdf)\n\n[code](cgan.py) - [gif result](https://mofanpy.com/static/results/gan/cgan/generating.gif)\n\n![](https://mofanpy.com/static/results/gan/cgan/generating.gif)\n\n## ACGAN\n[Conditional Image Synthesis with Auxiliary Classifier GANs](https://arxiv.org/pdf/1610.09585.pdf)\n\n[code](acgan.py) - [gif result](https://mofanpy.com/static/results/gan/acgan/generating.gif)\n\n![](https://mofanpy.com/static/results/gan/acgan/generating.gif)\n\n## InfoGAN\n[InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://arxiv.org/pdf/1606.03657.pdf)\n\n[code](infogan.py) - [gif result](https://mofanpy.com/static/results/gan/infogan/generating.gif)\n\n![](https://mofanpy.com/static/results/gan/infogan/generating.gif)\n\n## StyleGAN\n[A Style-Based Generator Architecture for Generative Adversarial Networks](https://arxiv.org/pdf/1812.04948.pdf)\n\n[code](stylegan.py) - [gif result](https://mofanpy.com/static/results/gan/stylegan/generating.gif)\n\n![](https://mofanpy.com/static/results/gan/stylegan/generating.gif)\n\n## CCGAN\n[Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks](https://arxiv.org/pdf/1611.06430.pdf)\n\n[code](ccgan.py) - [gif result](https://mofanpy.com/static/results/gan/ccgan/generating.gif)\n\n![](https://mofanpy.com/static/results/gan/ccgan/generating.gif)\n\n## Pix2Pix\n[Image-to-Image Translation with Conditional Adversarial Networks](https://arxiv.org/pdf/1611.07004.pdf)\n\n[code](pix2pix.py) - [gif result](https://mofanpy.com/static/results/gan/pix2pix/generating.gif)\n\n![](https://mofanpy.com/static/results/gan/pix2pix/generating.gif)\n\n## CycleGAN\n[Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://arxiv.org/pdf/1703.10593)\n\n[code](cyclegan.py) - [gif result](https://mofanpy.com/static/results/gan/cyclegan/generating.gif)\n\n![](https://mofanpy.com/static/results/gan/cyclegan/generating.gif)\n\n\n## SRGAN\n[Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://openaccess.thecvf.com/content_cvpr_2017/papers/Ledig_Photo-Realistic_Single_Image_CVPR_2017_paper.pdf)\n\n[code](srgan.py) - [gif result](https://mofanpy.com/static/results/gan/srgan/generating.gif)\n\n![](https://mofanpy.com/static/results/gan/srgan/generating.gif)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmorvanzhou%2Fmnistgans","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmorvanzhou%2Fmnistgans","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmorvanzhou%2Fmnistgans/lists"}