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arXiv:1701.00160cs.LG

NIPS 2016 Tutorial: Generative Adversarial Networks

Ian Goodfellow

Abstract

This report summarizes the tutorial presented by the author at NIPS 2016 on generative adversarial networks (GANs). The tutorial describes: (1) Why generative modeling is a topic worth studying, (2) how generative models work, and how GANs compare to other generative models, (3) the details of how GANs work, (4) research frontiers in GANs, and (5) state-of-the-art image models that combine GANs with other methods. Finally, the tutorial contains three exercises for readers to complete, and the solutions to these exercises.

arXiv comments
v2-v4 are all typo fixes. No substantive changes relative to v1
Journal reference
DOI
Subjects
cs.LGMachine Learning

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Linked by 3 identifiable commenters in 3 comments across 3 threads, from Jan 2017 to Jan 2017. Found in 4 HN submissions from Jan 2017 to Jan 2017. Open submission discussions.

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Discussed in comments3 threads

  1. Darpa Goes “Meta” with Machine Learning for Machine Learning (2016) 1 comment

    • GANs (and InfoGAN[0] in particular) has shown there are still pretty huge breakthoughs out there. Some other things you may be interested in: Learning to learn by gradient descent by gradient descent[1] Neural Architecture Search with Reinforcement Learning[2] [0]…

      nlView on HNParent context

  2. Deep Text Correcter 1 comment

    • Interesting idea! I think this is analogous to the idea of a de-noising autoencoder in computer vision. Here, instead of introducing Gaussian noise at the pixel level and using a CNN, you're introducing grammatical "noise" at the world level and using an LSTM. I think that…

      brandonbView on HNParent context

  3. Machine Learning and Likelihood Free Inference in Particle Physics 1 comment

    • Lots of topics and links throughout. Approximate Bayesian Computation website does a good job of framing what is meant by likelihood free inference. https://approximatebayesiancomputational.wordpress.com/paper... Here's an alternative technique for likelihood free inference…

      cranmerView on HNParent context

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