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Auditing Metrics for Synthetic Data Utility: A Cross-Domain Study of Appearance and Geometry
We propose SADGE, a quantitative similarity metric that predicts the performance of synthetic image datasets for common computer vision tasks without downstream model training. Estimating whether a synthetic dataset will lead to a model that performs well on real-world data remains a bottleneck in m
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