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:
Find out more about how DeepMind increases access to science here:
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 Everingham Prize in 2017 for contributions to the computer vision community.
Mihaela Rosca is a Research Engineer at DeepMind and PhD student at UCL, focusing on generative models research and probabilistic modelling, from variational inference to generative adversarial networks and reinforcement learning. Prior to joining DeepMind, she worked for Google on using deep learning to solve natural language processing tasks. She has an MEng in Computing from Imperial College London.
About the lecture series:
The Deep Learning Lecture Series is a collaboration between DeepMind and the UCL Centre for Artificial Intelligence. Over the past decade, Deep Learning has evolved as the leading artificial intelligence paradigm providing us with the ability to learn complex functions from raw data at unprecedented accuracy and scale. Deep Learning has been applied to problems in object recognition, speech recognition, speech synthesis, forecasting, scientific computing, control and many more. The resulting applications are touching all of our lives in areas such as healthcare and medical research, human-computer interaction, communication, transport, conservation, manufacturing and many other fields of human endeavour. In recognition of this huge impact, the 2019 Turing Award, the highest honour in computing, was awarded to pioneers of Deep Learning.
In this lecture series, research scientists from leading AI research lab, DeepMind, deliver 12 lectures on an exciting selection of topics in Deep Learning, ranging from the fundamentals of training neural networks via advanced ideas around memory, attention, and generative modelling to the important topic of responsible innovation.