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Introduction to Generative Adversarial Networks
Ian Goodfellow

Fri Dec 09 12:30 AM -- 01:00 AM (PST) @ None

Generative adversarial networks are deep models that learn to generate samples drawn from the same distribution as the training data. As with many deep generative models, the log-likelihood for a GAN is intractable. Unlike most other models, GANs do not require Monte Carlo or variational methods to overcome this intractability. Instead, GANs are trained by seeking a Nash equilibrium in a game played between a discriminator network that attempts to distinguish real data from model samples and a generator network that attempts to fool the discriminator. Stable algorithms for finding Nash equilibria remain an important research direction. Like many other models, GANs can also be applied to semi-supervised learning.

Author Information

Ian Goodfellow (OpenAI)

Ian Goodfellow is a research scientist at OpenAI. He obtained a B.Sc. and M.Sc. from Stanford University in 2009. He worked on the Stanford AI Robot and interned at Willow Garage before beginning to study deep learning under the direction of Andrew Ng. He completed a PhD co-supervised by Yoshua Bengio and Aaron Courville in 2014. He invented generative adversarial networks shortly after completing his thesis and shortly before joining Google Brain. At Google, he co-developed an end-to-end deep learning system for recognizing addresses in Street View, studied machine learning security and privacy, and co-authored the MIT Press textbook, Deep Learning. In 2016 he left Google to join OpenAI, a non-profit whose machine is to build safe AI for the benefit of everyone.

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