Generative Models for Image Synthesis

Lecture / Panel
Open to the Public

Geometric abstract image of brain


Part of the Special ECE Seminar Series 

Modern Artificial Intelligence


Generative Models for Image Synthesis


Jan Kautz, NVIDIA


Recent progress in generative models and particularly generative adversarial networks (GANs) has been remarkable. They have been shown to excel at image synthesis as well as image-to-image translation problems. I will present a number of our recent methods in this space, which, for instance, can translate images from one domain (e.g., day time) to another domain (e.g., night time) in an unsupervised fashion, synthesize completely new images, and even learn to detect defects by synthesizing their own training data.


Jan KautzJan Kautz is VP of Learning and Perception Research at NVIDIA. Jan and his team pursue fundamental research in the areas of computer vision and deep learning, including visual perception, geometric vision, generative models, and efficient deep learning. His and his team's work has been recognized with various awards and has been regularly featured in the media. Before joining NVIDIA in 2013, Jan was a tenured faculty member at University College London. He holds a BSc in Computer Science from the University of Erlangen-Nürnberg (1999), an MMath from the University of Waterloo (1999), received his PhD from  the Max-Planck-Institut für Informatik (2003), and worked as a post-doctoral researcher at the Massachusetts Institute of Technology (2003-2006).