I. Goodfellow. (2016)cite arxiv:1701.00160Comment: v2-v4 are all typo fixes. No substantive changes relative to v1.
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.
%0 Generic
%1 goodfellow2016tutorial
%A Goodfellow, Ian
%D 2016
%K adversarial deeplearning gan generative neuralnet
%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 = {2017-04-20T12:26:56.000+0200},
author = {Goodfellow, Ian},
biburl = {https://www.bibsonomy.org/bibtex/2ad118b533a7c03c6ce106341cffcd552/albinzehe},
description = {[1701.00160] NIPS 2016 Tutorial: Generative Adversarial Networks},
interhash = {9f6f0c2f9a255274f25f90c24f25ea1c},
intrahash = {ad118b533a7c03c6ce106341cffcd552},
keywords = {adversarial deeplearning gan generative neuralnet},
note = {cite arxiv:1701.00160Comment: v2-v4 are all typo fixes. No substantive changes relative to v1},
timestamp = {2017-04-24T09:28:37.000+0200},
title = {NIPS 2016 Tutorial: Generative Adversarial Networks},
url = {http://arxiv.org/abs/1701.00160},
year = 2016
}