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Innovations in Visual Prediction with JEPA
This chapter explores the JEPA model, which shifts focus from pixel-level prediction to predicting encoded representations in an embedding space for improved efficiency. It discusses the adaptation of masking techniques from NLP for computer vision tasks and the importance of collaborative training of encoders and predictors to enhance semantic understanding. The chapter also examines ongoing research into optimal masking strategies to enrich semantic representations in AI models, emphasizing efficient learning amidst the challenges of representation collapse.