  Deep learning models frequently suffer from various problems such as class
imbalance and lack of robustness to distribution shift. It is often difficult
to find data suitable for training beyond the available benchmarks. This is
especially the case for computer vision models. However, with the advent of
Generative Adversarial Networks (GANs), it is now possible to generate
high-quality synthetic data. This synthetic data can be used to alleviate some
of the challenges faced by deep learning models. In this work we present a
detailed analysis of the effect of training computer vision models using
different proportions of synthetic data along with real (organic) data. We
analyze the effect that various quantities of synthetic data, when mixed with
original data, can have on a model's robustness to out-of-distribution data and
the general quality of predictions.
