JEPA: Learning by Predicting Representations

Most self-supervised models either reconstruct pixels, wasting capacity on detail nobody can predict, or contrast augmented views, baking in hand-crafted bias. JEPA takes a third path: predict the representation of what is hidden, so the model can discard the unpredictable. This cursus builds it from the ground up: why predicting representations beats predicting pixels, then the machine itself, encoders, predictors, and the collapse problem that asymmetry and stop-gradients defeat, and finally I-JEPA and V-JEPA 2, where the idea becomes a world model that plans a robot's actions.