Exécution de notre outil de synthèse sur un nouvel article

Résumé : In this chapter we will explore the different types of synthetic data, how to generate fair synthetic data, and the benefits and challenges presented by synthetic data. We will also explore the synthetic-real field gap and how to overcome it with field transfer, field adaptation, and field randomization. We will also discuss how simulation is used to automate data labeling in autonomous vehicle companies and how real-world experience is inevitable. Finally, we will discuss how data can be pre-trained, learned with reinforcement, and self-supervised learning to learn medical images.