%0 Generic %1 goodfellow2016tutorial %A Goodfellow, Ian %D 2016 %K gans generative machine-learning %T NIPS 2016 Tutorial: Generative Adversarial Networks %U http://arxiv.org/abs/1701.00160 %X 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.

@misc{goodfellow2016tutorial, 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.}, added-at = {2017-06-30T21:22:50.000+0200}, author = {Goodfellow, Ian}, biburl = {https://www.bibsonomy.org/bibtex/2ad118b533a7c03c6ce106341cffcd552/hprop}, interhash = {9f6f0c2f9a255274f25f90c24f25ea1c}, intrahash = {ad118b533a7c03c6ce106341cffcd552}, keywords = {gans generative machine-learning}, note = {cite arxiv:1701.00160Comment: v2-v4 are all typo fixes. No substantive changes relative to v1}, timestamp = {2017-06-30T21:23:19.000+0200}, title = {NIPS 2016 Tutorial: Generative Adversarial Networks}, url = {http://arxiv.org/abs/1701.00160}, year = 2016 }