Summary of Learning and Leveraging World Models in Visual Representation Learning, by Quentin Garrido et al.
Learning and Leveraging World Models in Visual Representation Learning
by Quentin Garrido, Mahmoud Assran, Nicolas Ballas, Adrien Bardes, Laurent Najman, Yann LeCun
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Joint-Embedding Predictive Architecture (JEPA) is a self-supervised approach that learns by leveraging a world model. The authors extend JEPA to predict various corruptions in an input image, introducing Image World Models (IWMs). IWMs learn to predict global photometric transformations in latent space, relying on three key aspects: conditioning, prediction difficulty, and capacity. The predictive world model learned by IWM can be fine-tuned for diverse tasks, matching or surpassing previous self-supervised methods. Additionally, IWM allows controlling the abstraction level of learned representations, enabling invariant or equivariant representations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The authors developed a new way to learn from images using a “world model.” This approach helps machines understand how to fix broken parts of an image and even predicts what would happen if we changed different things about the image. The team shows that this method is very good at solving problems and can be used for many different tasks. They also found that it’s possible to control how much information is lost when learning from these images. |
Keywords
* Artificial intelligence * Embedding * Latent space * Self supervised
