Improving Synthetic Data Quality through Data Pruning

Overview

Proposes ranking generated samples by kernel-density estimates in an embedding space shared with held-out real data. Uses toy shapes and highway imagery to examine sample quality, then compares CMMD as increasing amounts of low-ranked synthetic data are removed.