Masking and Predicting: JEPA's Path to Robust Image Understanding

JEPA tackles the challenge of limited image-text paired data by learning semantic representations in the embedding space rather than at the pixel level, thereby filtering out unnecessary noise and focusing on higher-level abstraction. The model uses EMA-based target encoding to prevent collapse, allowing effective self-supervised learning without explicit semantic supervision. This represents a paradigm shift in how visual representations can be learned efficiently from unlabeled images.