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A list of papers on Generative Adversarial (Neural) Networks
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| 2026-07-31 | 3774 |
| 2026-08-03 | 3774 |
| 2026-08-05 | 3774 |
| 2026-08-06 | 3774 |
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# really-awesome-gan A list of papers and other resources on Generative Adversarial (Neural) Networks. This site is maintained by Holger Caesar. To complement or correct it, please contact me at holger-at-it-caesar.com or visit [it-caesar.com](http://www.it-caesar.com). Also checkout [really-awesome-semantic-segmentation](https://github.com/nightrome/really-awesome-semantic-segmentation) and our [COCO-Stuff dataset](https://github.com/nightrome/cocostuff). **NOTE:** Despite the enormous interest in this cite (~3000 visitors per month), I will no longer add new papers starting from November 2017. I feel that GANs have come from an exotic topic to the mainstream and an exhaustive list of all GAN papers is no more feasible or useful. However, I invite other people to continue this effort and reuse my list. ## Contents - [Recommendations](#recommendations) - [Workshops](#workshops) - [Tutorials & Workshops & Blogs](#tutorials--workshops--blogs) - [Videos](#videos) - [Code](#code) - [Papers](#papers) - [Overview](#overview) - [Theory & Machine Learning](#theory--machine-learning) - [Applied Vision](#applied-vision) - [Applied Other](#applied-other) - [Humor](#humor) ## Recommendations <ul> <li>Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis <a href="https://arxiv.org/abs/1704.04086">[arXiv]</a> <img src="http://it-caesar.com/github/beyond-face-rotation.png" alt="Beyond face rotation"></li> <li>Pose Guided Person Image Generation <a href="https://arxiv.org/abs/1705.09368">[arXiv]</a> <img src="http://it-caesar.com/github/pose-guided-person.png" alt="Pose guided person"></li> <li>Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks <a href="https://arxiv.org/abs/1703.10593">[arXiv]</a> <img src="http://it-caesar.com/github/cycle-gan.png" alt="Cycle GAN"></li> </ul> # Tutorials & Workshops & Blogs - Columbia Advanced Machine Learning Seminar - New Progress on GAN Theory and Practice [[Blog]](https://casmls.github.io/general/2017/04/13/gan.html) - Implicit Generative Models — What are you GAN-na do? [[Blog]](https://casmls.github.io/general/2017/05/24/ligm.html) - How to Train a GAN? Tips and tricks to make GANs work [[Blog]](https://github.com/soumith/ganhacks) - NIPS 2016 Tutorial: Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1701.00160) - NIPS 2016 Workshop on Adversarial Training [[Web]](https://sites.google.com/site/nips2016adversarial/) [[Blog]](http://www.inference.vc/my-summary-of-adversarial-training-nips-workshop/) - On the intuition behind deep learning & GANs — towards a fundamental understanding [[Blog]](https://blog.waya.ai/introduction-to-gans-a-boxing-match-b-w-neural-nets-b4e5319cc935) - OpenAI - Generative Models [[Blog]](https://openai.com/blog/generative-models/) - SimGANs - a game changer in unsupervised learning, self driving cars, and more [[Blog]](https://blog.waya.ai/simgans-applied-to-autonomous-driving-5a8c6676e36b) - Deep Diving into GANs: from theory to production (EuroScipy 2018) [[GitHub]](https://github.com/zurutech/gans-from-theory-to-production) # Books - GANs in Action: Deep learning with Generative Adversarial Networks [[Book]](https://www.manning.com/books/gans-in-action) # Videos - Generative Adversarial Networks by Ian Goodfellow [[Video]](https://channel9.msdn.com/Events/Neural-Information-Processing-Systems-Conference/Neural-Information-Processing-Systems-Conference-NIPS-2016/Generative-Adversarial-Networks) - Tutorial on Generative Adversarial Networks by Mark Chang [[Video]](https://www.youtube.com/playlist?list=PLeeHDpwX2Kj5Ugx6c9EfDLDojuQxnmxmU) - Deep Diving into GANs: From Theory to Production (EuroSciPy 2018) by Michele De Simoni, Paolo Galeone [[Video]](https://www.youtube.com/watch?v=CePrdabdtxw) # Code - Cleverhans: A library for benchmarking vulnerability to adversarial examples [[Code]](https://github.com/openai/cleverhans) [[Blog]](http
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Maxime Vandegar
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Subeesh Vasu · KLA · India
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Zhedong Zheng · University of Macau · China
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Sai Raj Kishore · Canada
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Andy Li · United Kingdom
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Kamyar Nazeri · Harris Computer · Canada
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Bruno Degardin
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7377a56c7903ce8e, desc:a list of