{"id":13934855,"url":"https://github.com/diegoalejogm/gans","last_synced_at":"2026-01-02T07:03:19.622Z","repository":{"id":27991652,"uuid":"115131280","full_name":"diegoalejogm/gans","owner":"diegoalejogm","description":"Generative Adversarial Networks implemented in PyTorch and Tensorflow","archived":false,"fork":false,"pushed_at":"2022-12-08T09:35:50.000Z","size":27032,"stargazers_count":826,"open_issues_count":19,"forks_count":353,"subscribers_count":28,"default_branch":"master","last_synced_at":"2024-08-08T23:19:06.824Z","etag":null,"topics":["cyclegan","dcgans","gans","gans-collections","paper-implementations","pytorch","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/diegoalejogm.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}},"created_at":"2017-12-22T16:15:37.000Z","updated_at":"2024-07-25T08:51:44.000Z","dependencies_parsed_at":"2022-06-29T20:44:40.680Z","dependency_job_id":null,"html_url":"https://github.com/diegoalejogm/gans","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/diegoalejogm%2Fgans","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/diegoalejogm%2Fgans/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/diegoalejogm%2Fgans/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/diegoalejogm%2Fgans/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/diegoalejogm","download_url":"https://codeload.github.com/diegoalejogm/gans/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":226666437,"owners_count":17665030,"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":["cyclegan","dcgans","gans","gans-collections","paper-implementations","pytorch","tensorflow"],"created_at":"2024-08-07T23:01:16.729Z","updated_at":"2026-01-02T07:03:19.617Z","avatar_url":"https://github.com/diegoalejogm.png","language":"Jupyter Notebook","funding_links":[],"categories":["Jupyter Notebook"],"sub_categories":[],"readme":"#  gans: Generative Adversarial Networks\nMultiple Generative Adversarial Networks (GANs) implemented in PyTorch and Tensorflow.\n\n[Check out this blog post](https://medium.com/ai-society/gans-from-scratch-1-a-deep-introduction-with-code-in-pytorch-and-tensorflow-cb03cdcdba0f) for an introduction to Generative Networks. \n\n\n\u003cimg src=\".images/dcgan_mnist.gif\" width=\"275\"\u003e \u003cimg src=\".images/dcgan_cifar.gif\" width=\"275\"\u003e\n\n## Vanilla GANs\nVanilla GANs found in this project were developed based on the original paper [Generative Adversarial Networks](https://arxiv.org/abs/1406.2661) by Goodfellow et al.\n\nThese are trained on the [MNIST dataset](http://yann.lecun.com/exdb/mnist/), and learn to create hand-written digit images using a 1-Dimensional vector representation for 2D input images.\n- [PyTorch Notebook](https://github.com/diegoalejogm/gans/blob/master/1.%20Vanilla%20GAN%20PyTorch.ipynb)\n- [TensorFlow Notebook](https://github.com/diegoalejogm/gans/blob/master/1.%20Vanilla%20GAN%20TensorFlow.ipynb)\n\n\u003cimg src=\".images/vanilla_mnist_pt_raw.png\" width=\"300\"\u003e \u003cimg src=\".images/vanilla_mnist_pt.png\" width=\"300\"\u003e\n\n__MNIST-like generated images before \u0026 after training.__\n\n\n## DCGANs\nDeep Convolutional Generative Adversarial Networks (DCGANs) in this repository were developed based on the original paper [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434) by Radford et al.\n\nThese are trained on the [CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html) and the [MNIST](http://yann.lecun.com/exdb/mnist/) datasets. They use 3 dimensional representations for images (length x height x colors) directly for training.\n\n- [TensorFlow CIFAR10 Notebook](https://github.com/diegoalejogm/gans/blob/master/2.%20DC-GAN%20TensorFlow.ipynb)\n- [PyTorch CIFAR10 Notebook](https://github.com/diegoalejogm/gans/blob/master/2.%20DC-GAN%20PyTorch.ipynb)\n- [PyTorch MNIST Notebook](https://github.com/diegoalejogm/gans/blob/master/2.%20DC-GAN%20PyTorch-MNIST.ipynb)\n\n\u003cimg src=\".images/dcgan_cifar_pt_raw.png\" width=\"300\"\u003e \u003cimg src=\".images/dcgan_cifar_pt.png\" width=\"300\"\u003e\n\n__CIFAR-like generated images before \u0026 after training.__\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdiegoalejogm%2Fgans","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdiegoalejogm%2Fgans","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdiegoalejogm%2Fgans/lists"}