  Synthetic data is an increasingly popular tool for training deep learning
models, especially in computer vision but also in other areas. In this work, we
attempt to provide a comprehensive survey of the various directions in the
development and application of synthetic data. First, we discuss synthetic
datasets for basic computer vision problems, both low-level (e.g., optical flow
estimation) and high-level (e.g., semantic segmentation), synthetic
environments and datasets for outdoor and urban scenes (autonomous driving),
indoor scenes (indoor navigation), aerial navigation, simulation environments
for robotics, applications of synthetic data outside computer vision (in neural
programming, bioinformatics, NLP, and more); we also survey the work on
improving synthetic data development and alternative ways to produce it such as
GANs. Second, we discuss in detail the synthetic-to-real domain adaptation
problem that inevitably arises in applications of synthetic data, including
synthetic-to-real refinement with GAN-based models and domain adaptation at the
feature/model level without explicit data transformations. Third, we turn to
privacy-related applications of synthetic data and review the work on
generating synthetic datasets with differential privacy guarantees. We conclude
by highlighting the most promising directions for further work in synthetic
data studies.
