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Awesome JEPA - Joint Embedding Predictive Architecture

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A carefully curated collection of high-quality tools, libraries, research papers, projects, and tutorials centered around Joint Embedding Predictive Architecture (JEPA) — a self-supervised learning paradigm introduced by Yann LeCun and Meta AI that learns representations by predicting representations of the future from representations of the present, without reconstructing pixels or tokens. This repository serves as a comprehensive, well-organized knowledge hub for researchers and developers exploring the next frontier of self-supervised learning and representation learning.

JEPA represents a fundamental shift in how AI systems learn representations. Unlike traditional generative models that reconstruct inputs, JEPA learns to predict abstract representations of the future state of the world from abstract representations of the present. This approach enables more efficient learning, better generalization, and the ability to handle complex, high-dimensional data without the computational overhead of pixel-level reconstruction.

To keep the community up-to-date with the latest developments, this repository is continuously enriched with newly published JEPA-related papers, real-world use cases, and open-source implementations. From foundational architectures to advanced variants like Hierarchical JEPA (H-JEPA) and applications in vision, language, and multimodal learning, the collection aims to highlight both foundational ideas and emerging best practices.

Note

📢 Announcement: Our paper is now available on SSRN!
Title: A Survey on Joint Embedding Predictive Architectures and World Models
If you find this paper interesting, please consider citing our work. Thank you for your support!

@article{brotee2025survey,
  title={A Survey on Joint Embedding Predictive Architectures and World Models},
  author={Brotee, Shamyo and Chhetri, Gaurab and Polock, Sazzad Bin Bashar and Bellamkonda, Venkata Surya and Rafe, Amir and Das, Subasish},
  journal={Available at SSRN 5772122},
  year={2025}
}

Whether you are building self-supervised learning systems, researching representation learning, or experimenting with predictive architectures for vision, language, or multimodal tasks, this resource offers a centralized, evolving platform to explore the powerful and expanding universe of JEPA-based systems.

Last Updated

August 25, 2026 at 01:25:56 AM UTC

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Papers (238)

Library

Tutorial

Written Tutorials

Video Tutorials

Contributing

We welcome contributions to this repository! If you have a resource that you believe should be included, please submit a pull request or open an issue. Contributions can include:

  • New libraries or tools related to JEPA.
  • Tutorials or guides that help users understand and implement JEPA.
  • Research papers that advance the field of JEPA and self-supervised learning.
  • Any other resources that you find valuable for the community

How to Contribute

  1. Fork the repository.
  2. Create a new branch for your changes.
  3. Make your changes and commit them with a clear message.
  4. Push your changes to your forked repository.
  5. Submit a pull request to the main repository.

Before contributing, take a look at the existing resources to avoid duplicates.

License

This repository is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to share and adapt the material, provided you give appropriate credit, link to the license, and indicate if changes were made.

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A carefully curated collection of high-quality tools, libraries, research papers, projects, and tutorials centered around Joint Embedding Predictive Architecture (JEPA).

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