© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2022
N. Gürsakal et al.Synthetic Data for Deep Learning https://doi.org/10.1007/978-1-4842-8587-9_2

2. Foundations of Synthetic data

Necmi Gürsakal1  , Sadullah Çelik2 and Esma Birişçi2
(1)
Bursa, Turkey
(2)
Aydın, Turkey
 

This chapter explores the different types of synthetic data, how to generate fair synthetic data, and the benefits and challenges presented by synthetic data. It also explores the synthetic-real field gap and how to overcome it with field transfer, adaptation, and randomization. We discuss how simulation automates data labeling in autonomous vehicle companies and how real-world experience is inevitable. Finally, we discuss how data can be pretrained, ...

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