Really-Awesome-Gan

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really-awesome-gan is an open-source research and education tool that provides a curated list of papers and resources on Generative Adversarial Networks. It serves as a comprehensive collection for AI researchers.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is really-awesome-gan?

really-awesome-gan is a comprehensive, open-source resource maintained by Holger Caesar, offering a curated list of papers and other materials focused on Generative Adversarial (Neural) Networks. This GitHub repository provides an extensive collection for anyone interested in GANs, from theoretical foundations to applied vision and other applications. While the maintainer stopped adding new papers in November 2017 due to GANs becoming mainstream, the existing list remains a valuable archive. It includes recommendations, tutorials, workshops, blogs, videos, and code examples, making it an excellent starting point for understanding and exploring GAN technology.

Best used for

Ideal for professors and researchers who need to conduct literature reviews, understand the theoretical foundations of GANs, and find practical code implementations. Especially valuable for those seeking a historical overview of GAN research up to late 2017.

Common actions

research generative adversarial networks

find GAN papers

learn GAN concepts

explore GAN code

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Capabilities

Key features

Curated GAN paper list
Tutorials & workshops
Code examples
Video resources
Blog references

Target Audience

professor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

Is really-awesome-gan still actively updated with new papers?

No, the maintainer, Holger Caesar, stopped adding new papers to the list in November 2017. He noted that GANs had become a mainstream topic, making an exhaustive list unfeasible. However, the existing collection remains a valuable resource.

What kind of resources are included in really-awesome-gan?

The repository includes a wide range of resources such as academic papers (overview, theory, applied vision, applied other), recommendations, tutorials, workshops, blogs, videos, and code examples. It covers various aspects of Generative Adversarial Networks.

Can I contribute to the really-awesome-gan list?

While the original maintainer is no longer adding new papers, the project is open source on GitHub. Other people are invited to continue the effort and reuse the existing list, implying potential for community contributions or forks.

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