CVPR 2018 Tutorial on GANs

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, 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.

You can find the recording for the tutorial on Youtube:

• Part 1: Ian Goodfellow, Phillip Isola, Taesung Park and Jun-Yan Zhu
• Part 2: Sanjeev Arora and Emily Denton
• Part 3: Mihaela Rosca, Ming-Yu Liu, and Judy Hoffman
• Part 4: Xiaolong Wang, Stefano Ermon, Carl Vondrick, and Alexei A. Efros

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