Image domain transfer includes methods that transform an image based on an example, commonly used in photorealistic and artistic style transfer, as well as learning-based methods that learn a transfer function based on a training set. These are usually based on generative adversarial networks (GANs), and can be supervised or unsupervised as well as unimodal or multimodal. I will present a number of our recent methods in this space that can be used to translate, for instance, a label map to a realistic street image, a day time street image to a night time street image, a dog to different cat breeds, and many more.
Jan is VP of Learning and Perception Research at NVIDIA. He leads the Learning & Perception Research team, working predominantly on computer vision problems (from low-level vision through geometric vision to high-level vision), as well as machine learning problems (including deep reinforcement learning, generative models, and efficient deep learning). 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).