{"id":15677755,"url":"https://github.com/rahulbhalley/turing-gan","last_synced_at":"2025-05-07T01:22:18.984Z","repository":{"id":119389561,"uuid":"158585488","full_name":"RahulBhalley/turing-gan","owner":"RahulBhalley","description":"Source code for \"Training Generative Adversarial Networks Via Turing Test\".","archived":false,"fork":false,"pushed_at":"2020-05-29T13:42:39.000Z","size":389563,"stargazers_count":13,"open_issues_count":0,"forks_count":2,"subscribers_count":2,"default_branch":"pytorch","last_synced_at":"2025-03-31T04:51:30.253Z","etag":null,"topics":["cifar10","fashion-mnist","gan","generative-adversarial-network","lipschitz-functions","mnist","optimal-transport","pytorch","turing-gans","turing-test","wasserstein","wasserstein-gan"],"latest_commit_sha":null,"homepage":"https://arxiv.org/abs/1810.10948","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/RahulBhalley.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2018-11-21T17:42:04.000Z","updated_at":"2021-03-31T16:42:42.000Z","dependencies_parsed_at":null,"dependency_job_id":"0d19cb0d-bf74-4faa-8048-e7af91f2ad90","html_url":"https://github.com/RahulBhalley/turing-gan","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/RahulBhalley%2Fturing-gan","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RahulBhalley%2Fturing-gan/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RahulBhalley%2Fturing-gan/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RahulBhalley%2Fturing-gan/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/RahulBhalley","download_url":"https://codeload.github.com/RahulBhalley/turing-gan/tar.gz/refs/heads/pytorch","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252794291,"owners_count":21805173,"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":["cifar10","fashion-mnist","gan","generative-adversarial-network","lipschitz-functions","mnist","optimal-transport","pytorch","turing-gans","turing-test","wasserstein","wasserstein-gan"],"created_at":"2024-10-03T16:11:16.590Z","updated_at":"2025-05-07T01:22:18.966Z","avatar_url":"https://github.com/RahulBhalley.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Turing Generative Adversarial Network\nI found Turing GAN a way to train GANs quickly! This excited me to write my own versions in [PyTorch](https://pytorch.org) refering the original Keras [code](https://github.com/bojone/T-GANs).\n\n## Experiments\nSo, following are my experiments' resulting image data.\n\n### Note\n- For all the experiments the images shown below are sampled after 100K iterations of training the Turing GAN on various datasets. \n- All the experiments used spectral normalization for 1-Lipschitz contraint enforcement. \n- I trained all of the Turing GANs with both Jensen-Shannon and Wasserstein divergences.\n\n### CIFAR-10\n#### Turing Standard GAN with Spectral Normalization\n![](https://raw.githubusercontent.com/rahulbhalley/turing-gan.pytorch/pytorch/sgan/samples/cifar-10/latest_100000.png)\n#### Turing Wasserstein GAN with Spectral Normalization\n![](https://raw.githubusercontent.com/rahulbhalley/turing-gan.pytorch/pytorch/wgan/samples/cifar-10/latest_100000.png)\n\n### MNIST\n#### Turing Standard GAN with Spectral Normalization\n![](https://raw.githubusercontent.com/rahulbhalley/turing-gan.pytorch/pytorch/sgan/samples/mnist/latest_100000.png)\n#### Turing Wasserstein GAN with Spectral Normalization\n![](https://raw.githubusercontent.com/rahulbhalley/turing-gan.pytorch/pytorch/wgan/samples/mnist/latest_100000.png)\n\n### Fashion MNIST\n#### Turing Standard GAN with Spectral Normalization\n![](https://raw.githubusercontent.com/rahulbhalley/turing-gan.pytorch/pytorch/sgan/samples/fashion-mnist/latest_100000.png)\n#### Turing Wasserstein GAN with Spectral Normalization\n![](https://raw.githubusercontent.com/rahulbhalley/turing-gan.pytorch/pytorch/wgan/samples/fashion-mnist/latest_100000.png)\n\n## References\n- Training Generative Adversarial Networks Via Turing Test [[arXiv](https://arxiv.org/abs/1810.10948)]\n- Original [T-GANs](https://github.com/bojone/T-GANs) implementation\n- Spectral Normalization for Generative Adversarial Networks [[arXiv](https://arxiv.org/abs/1802.05957)]\n- Spectral Normalization [implementation](https://github.com/christiancosgrove/pytorch-spectral-normalization-gan/blob/master/spectral_normalization.py) in [PyTorch](https://pytorch.org)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frahulbhalley%2Fturing-gan","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frahulbhalley%2Fturing-gan","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frahulbhalley%2Fturing-gan/lists"}