A relative projection error metric in foundation-model embedding space predicts the downstream utility of synthetic positive samples for binary classifiers.
DC-cycleGAN: Bidirectional CT-to-MR Synthesis from Unpaired Data
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Discriminative Span as a Predictor of Synthetic Data Utility via Classifier Reconstruction
A relative projection error metric in foundation-model embedding space predicts the downstream utility of synthetic positive samples for binary classifiers.