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.
James DiCarlo Head, Department of Brain and Cognitive Sciences, Peter de Florez Professor of Neuroscience
Rama Ramakrishnan Professor of the Practice in Data Science and Applied Machine Learning, Sloan School of Management
Contact
Assistant
Principal Investigator Daniel Blankschtein
Principal Investigator John Gabrieli