Directions in Synthetic Data Development

Sergey I. Nikolenko ()
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Sergey I. Nikolenko: Synthesis AI

Chapter Chapter 9 in Synthetic Data for Deep Learning, 2021, pp 227-234 from Springer

Abstract: Abstract In this chapter, we outline the main directions that we believe to represent promising ways to further improve synthetic data, making it more useful for a wide variety of applications in computer vision and other fields. In particular, we discuss the idea of domain randomization (Section 9.1) intended to improve the applications of synthetic datasets, methods to improve CGI-based synthetic data generation itself (Section 9.2), ways to create synthetic data from real images by cutting and pasting (Section 9.3), and finally possibilities to produce synthetic data by generative models (Section 9.4). The latter means generating useful synthetic data from scratch rather than domain adaptation and refinement, which we consider in a separate Chapter 10 .

Date: 2021
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DOI: 10.1007/978-3-030-75178-4_9

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