{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/generative-adversarial-networks","title":"Generative Adversarial Networks","arxiv_id":"1406.2661","date":"2014-06-10","proceeding":"Proceedings of the 27th International Conference on Neural Information Processing Systems 2014 12","authors":["Ian J. Goodfellow","Jean Pouget-Abadie","Mehdi Mirza","Bing Xu","David Warde-Farley","Sherjil Ozair","Aaron Courville","Yoshua Bengio"],"abstract":"We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1/2 everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. 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GAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"generative-adversarial-networks","repo_url":"https://github.com/qinpengzhi/myDCGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"generative-adversarial-networks","repo_url":"https://github.com/rajprakrit/DCGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"generative-adversarial-networks","repo_url":"https://github.com/rdutta1999/Patched-Face-Regeneration-GAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"generative-adversarial-networks","repo_url":"https://github.com/remibasaru/GANs-Suite","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"generative-adversarial-networks","repo_url":"https://github.com/roberttwomey/machine-imagination-workshop","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"generative-adversarial-networks","repo_url":"https://github.com/rohitkuk/AnimeGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"generative-adversarial-networks","repo_url":"https://github.com/rohitkuk/Cartoonify","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"generative-adversarial-networks","repo_url":"https://github.com/safwankdb/Vanilla-GAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"generative-adversarial-networks","repo_url":"https://github.com/sandeeplbscek/awesome-deeplearning-resources","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"generative-adversarial-networks","repo_url":"https://github.com/saurabh23kgp/GAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"generative-adversarial-networks","repo_url":"https://github.com/seyedsaleh/music-generator","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"generative-adversarial-networks","repo_url":"https://github.com/singhsidhukuldeep/Generative-Adversarial-Network-GAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"generative-adversarial-networks","repo_url":"https://github.com/sliao-mi-luku/GAN-food-image-generator","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mxnet","reach":{"status":"ok"}},{"paper_slug":"generative-adversarial-networks","repo_url":"https://github.com/sooooner/MNIST-GAN-with-tensorflow2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"generative-adversarial-networks","repo_url":"https://github.com/stekern/terraform-aws-paper2remarkable-lambda","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"generative-adversarial-networks","repo_url":"https://github.com/subinium/Pytorch-GAN-Keynote-KR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"generative-adversarial-networks","repo_url":"https://github.com/suzana-ilic/DCGANs_pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"generative-adversarial-networks","repo_url":"https://github.com/suzana-ilic/pytorch_DCGANs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"generative-adversarial-networks","repo_url":"https://github.com/syahdeini/gan","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"generative-adversarial-networks","repo_url":"https://github.com/t0nberryking/DCGAN256","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"generative-adversarial-networks","repo_url":"https://github.com/tanjeffreyz/generative-adversarial-networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"generative-adversarial-networks","repo_url":"https://github.com/tryambak2019/SGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"generative-adversarial-networks","repo_url":"https://github.com/ttsalpha/generative-adversarial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