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Generative Adversarial Networks

Generative adversarial networks (GANs) use deep learning to imagine new, previously unseen data, such as images. GANs are based on a game between two players: a generator network that creates images, and a discriminator network that guesses whether images came from the training data or from the generator network. This game resembles the conflict between counterfeiters and the police, with counterfeiters forced to learn to produce realistic fakes. At equilibrium, the generator produces images that come from the same probability distribution as the training data, and the discriminator is unable to tell whether images are real or fake.

Ian Goodfellow, Research Scientist at OpenAI

Ian Goodfellow is a research scientist at OpenAI. He is the lead author of the MIT Press textbook Deep Learning. In addition to generative models, he also studies security and privacy for machine learning. He has contributed to open source libraries including TensorFlow, Theano, and Pylearn2. He obtained a PhD from University of Montreal in Yoshua Bengio's lab, and an MSc from Stanford University, where he studied deep learning and computer vision with Andrew Ng. He is generally interested in all things deep learning.