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Synthetic-to-Real Domain Adaptation and Refinement

In: Synthetic Data for Deep Learning

Author

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  • Sergey I. Nikolenko

    (Synthesis AI
    Steklov Institute of Mathematics)

Abstract

Domain adaptation is a set of techniques aimed to make a model trained on one domain of data to work well on a different target domain. In this chapter, we give a survey of domain adaptation approaches that have been used for synthetic-to-real adaptation, that is, methods for making models trained on synthetic data work well on real data, which is almost always the end goal. We distinguish two main approaches. In synthetic-to-real refinement input synthetic data is modified, usually to be made more realistic, and we can actually see the modified data. In model-based domain adaptation, it is the training process or the model structure that changes to ensure domain adaptation, while the data remains as synthetic as it has been. We will discuss neural architectures for both approaches, including many models based on generative adversarial networks.

Suggested Citation

  • Sergey I. Nikolenko, 2021. "Synthetic-to-Real Domain Adaptation and Refinement," Springer Optimization and Its Applications, in: Synthetic Data for Deep Learning, chapter 0, pages 235-268, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-75178-4_10
    DOI: 10.1007/978-3-030-75178-4_10
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