Training any AI model requires carefully labeled and diverse datasets that contain thousands to tens of millions of elements, some of which are beyond the visual spectrum. Collecting and labeling this data in the real world is time-consuming and expensive. This can hinder the development of AI models and slow down the time to solution.
Generated by computer simulations, synthetic data is comprised of 2D images or text, and can be used in conjunction with real-world data to train AI models. Synthetic data generation (SDG) can save significant time and greatly reduce costs.