  Recent breakthroughs in synthetic data generation approaches made it possible
to produce highly photorealistic images which are hardly distinguishable from
real ones. Furthermore, synthetic generation pipelines have the potential to
generate an unlimited number of images. The combination of high photorealism
and scale turn synthetic data into a promising candidate for improving various
machine learning (ML) pipelines. Thus far, a large body of research in this
field has focused on using synthetic images for training, by augmenting and
enlarging training data. In contrast to using synthetic data for training, in
this work we explore whether synthetic data can be beneficial for model
selection. Considering the task of image classification, we demonstrate that
when data is scarce, synthetic data can be used to replace the held out
validation set, thus allowing to train on a larger dataset. We also introduce a
novel method to calibrate the synthetic error estimation to fit that of the
real domain. We show that such calibration significantly improves the
usefulness of synthetic data for model selection.
