The Annotated JEPA

This post provides a detailed walkthrough of Joint Embedding Predictive Architectures (JEPAs), Yann LeCun's self-supervised learning approach that trains models to understand the world by predicting representations in latent space rather than reconstructing pixels. The explanation covers I-JEPA for images, its extensions to video (V-JEPA), and the latest variant LeJEPA, using concrete implementations to demonstrate how JEPA avoids trivial solutions and wasted capacity by focusing on meaningful semantic understanding rather than irrelevant pixel details.