Phillip Isola Associate Professor, Department of Electrical Engineering and Computer Science
Generative models can now produce realistic and diverse synthetic data in many domains. This makes them a viable choice as a data source for training downstream AI systems. Unlike real data, synthetic data can be steered and optimized via interventions in the generative process. I will share my view on how this makes synthetic data act like data++, data with additional capabilities. I will discuss the advantages and disadvantages of this setting, and show several applications toward problems in computer vision and robotics.