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The Predictability Bias of Joint-Embedding Predictive Learning in Medical Imaging
Joint-embedding predictive architectures (JEPAs) learn by predicting teacher representations of masked regions from visible context. Point prediction summarizes the conditional target distribution and cannot reproduce variation that the context does not resolve. We call this constraint predictabilit
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