  This paper demonstrates an approach for learning highly semantic image
representations without relying on hand-crafted data-augmentations. We
introduce the Image-based Joint-Embedding Predictive Architecture (I-JEPA), a
non-generative approach for self-supervised learning from images. The idea
behind I-JEPA is simple: from a single context block, predict the
representations of various target blocks in the same image. A core design
choice to guide I-JEPA towards producing semantic representations is the
masking strategy; specifically, it is crucial to (a) sample target blocks with
sufficiently large scale (semantic), and to (b) use a sufficiently informative
(spatially distributed) context block. Empirically, when combined with Vision
Transformers, we find I-JEPA to be highly scalable. For instance, we train a
ViT-Huge/14 on ImageNet using 16 A100 GPUs in under 72 hours to achieve strong
downstream performance across a wide range of tasks, from linear classification
to object counting and depth prediction.
