CVPR 2018 Tutorial on GANs

Salt Lake City, USA

June 22nd, 2018, Friday

NOTE: Registration should be done on CVPR. There is no need to register for this specific tutorial.

Generative adversarial networks (GANs) have been at the forefront of research on generative models in the last couple of years. GANs have been used for image generation, image processing, image synthesis from captions, image editing, visual domain adaptation, data generation for visual recognition, and many other applications, often leading to state of the art results.

This tutorial aims to provide a broad overview of generative adversarial networks, mainly including the following three parts:

  • Theoretical foundations such as basic concepts, mechanisms, and theoretical considerations
  • Best practices of the current state-of-the-art GAN and conditional GAN models, including network architectures, objective functions, and other training tricks
  • Computer vision applications including visual domain adaptation, image processing (e.g., restoration, inpainting, super-resolution), image synthesis and manipulation, video prediction and generation, 3D modeling, and synthetic data generation for visual recognition

For an example of the power of the generative adversarial networks, see the below result of generating high resolution realistic portraits and driving dash cam images.

Schedule

09:00 Introduction to Generative Adversarial Networks

Ian Goodfellow, Google Brain

09:30 Paired Image-to-Image Translation

Phillip Isola, MIT

10:00 Unpaired Image-to-Image Translation

Taesung Park, UC Berkeley and Jun-Yan Zhu, MIT

10:30 Coffee Break / Live Demo

11:00 Can GANs actually learn the distribution? Some obstacles.

Sanjeev Arora, Princeton

11:45 TBD

Emily Denton, NYU

12:15 Lunch break

13:30 Autoencoder, VAE, and GANs

Mihaela Rosca, Deepmind

14:00 TBD

Ming-Yu Liu, NVIDIA

14:30 Adversarial Domain Adaptation

Judy Hoffman, UC Berkeley

15:00 Coffee break / Live Demo

15:30 TBD

Abhinav Gupta, CMU

16:00 Generative Adversarial Imitation Learning

Stefano Ermon, Stanford

16:30 Video Generation and Prediction

Carl Vondrick, Columbia University

17:00 TBD

Alexei A. Efros, UC Berkeley

Organizers

This tutorial was organized by Jun-Yan Zhu, Taesung Park, Mihaela Rosca, Phillip Isola, and Ian Goodfellow.

Contact the organizers at cvpr2018gantutorial@googlegroups.com