Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
dc.contributor.author
Mescheder, Lars
dc.contributor.author
Nowozin, Sebastian
dc.contributor.author
Geiger, Andreas
dc.contributor.editor
Precup, Doina
dc.contributor.editor
Teh, Yee W.
dc.date.accessioned
2020-02-19T12:12:12Z
dc.date.available
2018-01-26T09:39:20Z
dc.date.available
2018-05-08T13:47:16Z
dc.date.available
2020-02-19T12:12:12Z
dc.date.issued
2017
dc.identifier.issn
2640-3498
dc.identifier.uri
http://hdl.handle.net/20.500.11850/235062
dc.language.iso
en
en_US
dc.publisher
PMLR
en_US
dc.title
Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
en_US
dc.type
Conference Paper
ethz.book.title
Proceedings of the 34th International Conference on Machine Learning (ICML 2017)
en_US
ethz.journal.title
Proceedings of Machine Learning Research
ethz.journal.volume
70
en_US
ethz.pages.start
2391
en_US
ethz.pages.end
2400
en_US
ethz.event
34th International Conference on Machine Learning (ICML 2017)
en_US
ethz.event.location
Sydney, Australia
en_US
ethz.event.date
August 6-11, 2017
en_US
ethz.publication.place
Cambridge, MA
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02659 - Institut für Visual Computing / Institute for Visual Computing::03766 - Pollefeys, Marc / Pollefeys, Marc
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02659 - Institut für Visual Computing / Institute for Visual Computing::03766 - Pollefeys, Marc / Pollefeys, Marc
ethz.identifier.url
http://proceedings.mlr.press/v70/mescheder17a.html
ethz.date.deposited
2018-01-26T09:39:21Z
ethz.source
BATCH
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2018-05-08T13:47:18Z
ethz.rosetta.lastUpdated
2021-02-15T08:09:41Z
ethz.rosetta.versionExported
true
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Conference Paper [34974]