  Joint-Embedding Predictive Architecture (JEPA) has emerged as a promising
self-supervised approach that learns by leveraging a world model. While
previously limited to predicting missing parts of an input, we explore how to
generalize the JEPA prediction task to a broader set of corruptions. We
introduce Image World Models, an approach that goes beyond masked image
modeling and learns to predict the effect of global photometric transformations
in latent space. We study the recipe of learning performant IWMs and show that
it relies on three key aspects: conditioning, prediction difficulty, and
capacity. Additionally, we show that the predictive world model learned by IWM
can be adapted through finetuning to solve diverse tasks; a fine-tuned IWM
world model matches or surpasses the performance of previous self-supervised
methods. Finally, we show that learning with an IWM allows one to control the
abstraction level of the learned representations, learning invariant
representations such as contrastive methods, or equivariant representations
such as masked image modelling.
