Welcome to the Generative AI Papers Repository! This repository is dedicated to compiling and sharing research papers that are trending/impactful in the domain of Generative AI. This compilation in part motivated by the course CSE 598: Topics in Generative AI by Dr. Yezhou Yang, Arizona State University.
Welcome to the Generative AI Papers Repository! This repository is dedicated to compiling and sharing research papers in the domain of Generative AI.
This repository aims to serve as a comprehensive collection of significant research papers in the field of Generative AI. It is intended to be a valuable resource for students, researchers, and practitioners who are interested in exploring the latest advancements and foundational works in this exciting area of artificial intelligence.
Contributions to this repository are welcome from anyone. If you have a research paper that you believe should be included, please follow the contribution guidelines outlined in the CONTRIBUTING.md file. Your contributions will help in creating a rich and diverse repository of knowledge.
To contribute to this repository, please adhere to the following guidelines:
- Fork the Repository: Create a fork of this repository to your GitHub account.
- Add Your Paper: Add the research paper to the appropriate directory. Ensure that the paper is properly cited and includes all necessary metadata.
- Submit a Pull Request: Once you have added the paper, submit a pull request for review. Please provide a brief description of the paper and its significance in the pull request.
- Follow the Code of Conduct: Ensure that your contributions adhere to the repository's code of conduct, which promotes respectful and constructive interactions.
For detailed instructions, please refer to the CONTRIBUTING.md file.
| Title of Paper | Date | Arxiv/Conference Published |
|---|---|---|
| Generative Adversarial Networks | June 2014 | NeurIPS 2014 |
| Auto-Encoding Variational Bayes - Variational Autoendcoders | December 2013 | arxiv |
| Pixel Recurrent Neural Network PixelRNN | January 2016 | ICML 2016 |
We chose the MIT License for its simplicity and permissiveness. It allows users to freely use, modify, and distribute the code with minimal restrictions, fostering innovation and collaboration while providing legal protection for contributors.
For any questions or suggestions, please feel free to open an issue or contact the repository maintainers.