Schedule and slides

22 October 2017

9:15 Ian Goodfellow, Google Brain: Introduction [slides (pdf)] [slides (keynote)]

9:45 Mihaela Rosca, DeepMind: Autoencoder GANs [slides (pdf)]

10:15 Coffee break and live demo session: Jun-Yan Zhu and Taesung Park: iGAN / pix2pix / CycleGAN

11:00 Soumith Chintala, Facebook: GANs in the Wild [slides (pdf)]

11:30 Han Zhang, Rutgers University: Conditional GANs, StackGAN [slides (pdf)]

12:00 Lucas Theis, Twitter: Evaluating Generative Models [slides (pdf)]

12:30 Lunch

14:00 Sanjeev Arora, Princeton University : Do GANs learn the distribution? [slides (pptx)]

14:45 Victor Lempitsky, Skoltech: Domain Adversarial Learning [slides (pdf)]

15:15 Jun-Yan Zhu, UC Berkeley: Visual Synthesis and Manipulation with GANs [slides(pdf)] [slides(pptx)]

15:45 Coffee break and live demo session: Jun-Yan Zhu and Taesung Park: iGAN / pix2pix / CycleGAN

16:30 David Pfau, DeepMind: Connections between adversarial training and RL

17:00 Alexei Efros, UC Berkeley: GANs as Learned Loss Functions