DeepMind x UCL | Deep Learning Lectures | 9/12 | Generative Adversarial Networks

Generative adversarial networks (GANs), first proposed by Ian Goodfellow et al. in 2014, have emerged as one of the most promising approaches to generative modeling, particularly for image synthesis. In their most basic form, they consist of two "competing" networks: a generator which tries to produce data resembling a given data distribution (e.g., images), and a discriminator which predicts whether its inputs come from the real data distribution or from the generator, guiding the generator to produce increasingly realistic samples as it learns to "fool" the discriminator more effectively. This lecture discusses the theory behind these models, the difficulties involved in optimising them, and theoretical and empirical improvements to the basic framework. It also discusses state-of-the-art applications of this framework to other problem formulations (e.g., CycleGAN), domains (e.g., video and speech synthesis), and their use for representation learning (e.g., VAE-GAN hybrids, bidirectional GAN). Note: this lecture was originally advertised as number 11 in the series. Download the slides here: https://storage.googleapis.com/deepmind-media/UCLxDeepMind_2020/L9%20-%20UCLxDeepMind%20DL2020.pdf Find out more about how DeepMind increases access to science here: https://deepmind.com/about#access_to_science Speaker Bios: Jeff Donahue is a research scientist at DeepMind on the Deep Learning team, currently focusing on adversarial generative models and unsupervised representation learning. He has worked on the BigGAN, BigBiGAN, DVD-GAN, and GAN-TTS projects. He completed his Ph.D. at UC Berkeley, focusing on visual representation learning, with projects including DeCAF, R-CNN, and LRCN, some of the earliest applications of transferring deep visual representations to traditional computer vision tasks such as object detection and image captioning. While at Berkeley he also co-led development of the Caffe deep learning framework, which was awarded with the Mark Everingha