I-JEPA learns visual features by predicting the representations of hidden image regions, rather than reconstructing their pixels. This guide explains the context encoder, target encoder, predictor, and masking strategy, then examines what the paper's accuracy and efficiency results actually measure.

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Self-distillation with no labels (DINO) trains a student network to match an exponential-moving-average teacher across differently augmented views of the same image. Applied to Vision Transformers, it produces features that perform strongly with simple linear and k-nearest-neighbor classifiers. Its final-layer self-attention also reveals object boundaries without segmentation labels, although DINO is not itself a segmentation model.

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