The future of robots and perception systems with synthetic data
Collecting and annotating data is a time-consuming and expensive process, and to ensure models can generalize well, the data must be diverse and balanced. Recent advancements in simulation tools and generative models have led many computer vision AI practitioners to consider synthetic data as a possible alternative to real data. In this story, Ekaterina Sirazitdinova of NVIDIA discusses the benefits and challenges of synthetic data and will share a typical workflow of synthetic data creation.
Testing and improving AI models
Synthetic data and domain randomization can be used for testing robots in different environments and setups, simulating scenes and behaviors using virtual assets.
It is important to use real data to test synthetically trained models to ensure they can generalize well to real-world scenarios.
Synthetic data is annotated information that computer simulations or algorithms generate as an alternative to real world data. – Ekaterina S.
The role of simulation tools and generative models
Recent advancements in simulation tools and generative models have made it possible to create synthetic data for training AI models.
Techniques like domain adaptation and generative AI models like diffusion and stable diffusion edify can be used to generate synthetic data.
