The JEPA Predictor: A Transferable Operator for Occluded Feature Completion
Joint-Embedding Predictive Architectures (JEPAs) train a predictor jointly with their encoder, but downstream deployment discards the predictor and reads features from the encoder alone. The predictor is, by construction, a learned operator from visible-context features to features at masked positions, the structure a partial-view classifier needs. We show that this operator is portable across encoder families. We first establish that, at heavy mask, retaining the frozen predictor on a JEPA encoder substantially closes the accuracy gap against the strongest non-JEPA discriminative baselines. We then bolt the frozen predictors of I-JEPA and V-JEPA 2 onto four non-JEPA hosts (CLIP, DINOv3, ...
| Authors | William Nguyen, Christopher Nguyen |
|---|---|
| Primary category | cs.CV |
| Published | 8 July 2026 |
| Last updated at source | 2 October 2026 |
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