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Generating synthetic data through generative models is gaining interest inthe ML community and beyond, promising a future where datasets can be tailoredto individual needs. Unfortunately, synthetic data is usually not perfect,resulting in potential errors in downstream tasks. In this work we explore howthe generative process affects the downstream ML task. We show that the naivesynthetic data approach -- using synthetic data as if it is real -- leads todownstream models and analyses that do not generalize well to real data. As afirst step towards better ML in the synthetic data regime, we introduce DeepGenerative Ensemble (DGE) -- a framework inspired by Deep Ensembles that aimsto implicitly approximate the posterior distribution over the generativeprocess model parameters. DGE improves downstream model training, evaluation,and uncertainty quantification, vastly outperforming the naive approach onaverage. The largest improvements are achieved for minority classes andlow-density regions of the original data, for which the generative uncertaintyis largest.

Synthetic data, real errors: how (not) to publish and use synthetic data