The GAN Zoo
A running list of named GAN (generative adversarial network) research papers, with links.
- Stars
- 14.7k
- Forks
- 2.5k
- Licence
- MIT
- Language
- Python
- Last update
- 3.1 years ago
- Type
- Curated list
No commits in over a year — check for forks or alternatives before adopting.
What it is
The GAN Zoo is a curated list of research papers that propose named variants of GANs, a type of machine learning model that learns to generate realistic data such as images. Each entry links to the paper and, where the author provided one, to the code. The same data is also available as a tab-separated file that can be filtered by year or searched by title.
- Search the gans.tsv file by title to find a specific GAN paper
- Filter the tabular data by year to see what was published in a given period
- Follow links to arXiv papers and linked GitHub code for a GAN variant you are studying
- Submit a pull request to add a newly published GAN to the list
Great for
- Researchers tracking the many named GAN variants in the literature
- Students looking for an overview of GAN papers to read
- Engineers searching for a paper or code for a specific GAN variant by name
Not ideal for
- People who want a ready-to-run tool, since this is a list of links rather than software
- Readers wanting a beginner explanation of how GANs work, because the list only names and links papers
How to get it
- 1
Read it online
Clone the repository
git clone https://github.com/hindupuravinash/the-gan-zoo - 2
Run it
- Open the repository page at https://github.com/hindupuravinash/the-gan-zoo
- Read the list in the README or open the gans.tsv file for the tabular version
- Follow a paper link to read the original work
- 3
Get the most out of it
- Use the gans.tsv file to filter by year or search by title
- Follow the linked GitHub repositories to find runnable code for specific GAN variants
- Contribute missing GANs through pull requests to gans.tsv in the same format
Downloads
Strengths
- Large, long-running collection that the author says is updated as new papers appear
- Each entry links directly to the source paper, and many to code
- Simple to browse, with a searchable tabular version
Trade-offs
- It is a list of links, so it does not explain the methods or evaluate the papers
- The README is truncated here, so the full scope and update frequency cannot be confirmed from this text
- Some entries have the same name for different models, such as two entries named BCGAN and two named Bayesian GAN
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| the-gan-zoo (this) | 14.7k | Curated list | beginner | 3.1 years ago | Researchers tracking the many named GAN variants in the literature |
| AI-For-Beginners | 70.5k | Learning resource | beginner | 14 days ago | Beginners who want a structured, self-paced introduction to AI |
| annotated_deep_learning_paper_implementations | 67.6k | Learning resource | intermediate | 4 days ago | Students learning how transformers, GANs, or optimizers work internally |
| fashion-mnist | 12.9k | Learning resource | beginner | 4.4 years ago | Machine learning students comparing classifiers on a familiar format |
| applied-ml | 30.8k | Curated list | beginner | 2.3 years ago | Engineers planning an ML project who want to see how other companies approached a similar problem |
| NLP-progress | 23k | Curated list | beginner | 2.3 years ago | Researchers looking for benchmark datasets for a specific NLP task |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.