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@misc{SMALE,
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@article{CONSTRAINTS,
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	title = "Adversarial examples from computational constraints",
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	adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180510204B},
	adsnote = {Provided by the SAO/NASA Astrophysics Data System},
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}
@article{GANSEQUAL,
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	title = "Are GANs Created Equal? A Large-Scale Study",
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}
@article{DISCONNECTED,
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	keywords = {Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning},
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@article{AUDIOGAN,
	author = {Chris Donahue and Julian McAuley and Miller Puckette},
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	year = {2018},
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@article{MASKGAN,
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}
@article{GEOMETRICDEEPLEARNING,
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@article{NETGAN,
	author = {Bojchevski, A. and Shchur, O. and Zugner, D. and Gunnemann, S.},
	title = "NetGAN: Generating Graphs via Random Walks",
	journal = {ArXiv e-prints},
	archiveprefix = "arXiv",
	eprint = {1803.00816},
	primaryclass = "stat.ML",
	keywords = {Statistics - Machine Learning, Computer Science - Machine Learning, Computer Science - Social and Information Networks},
	year = 2018,
	month = mar,
	adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	url = {https://arxiv.org/abs/1803.00816}
}
@inproceedings{LOCALLYSTABLE,
	title = {Gradient descent GAN optimization is locally stable},
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	booktitle = {Advances in Neural Information Processing Systems},
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	year = {2017},
	url = {https://arxiv.org/abs/1706.04156}
}
@inproceedings{WHICHMETHODSCONVERGE,
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	pages = {3481--3490},
	year = {2018},
	editor = {Dy, Jennifer and Krause, Andreas},
	volume = {80},
	series = {Proceedings of Machine Learning Research},
	address = {Stockholmsmässan, Stockholm Sweden},
	month = {10--15 Jul},
	publisher = {PMLR},
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	url = {http://proceedings.mlr.press/v80/mescheder18a.html}
}
@article{LIMITATIONS,
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	title = "On the Limitations of First-Order Approximation in GAN Dynamics",
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	archiveprefix = "arXiv",
	eprint = {1706.09884},
	keywords = {Computer Science - Machine Learning, Computer Science - Data Structures and Algorithms},
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	adsurl = {http://adsabs.harvard.edu/abs/2017arXiv170609884L},
	adsnote = {Provided by the SAO/NASA Astrophysics Data System},
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}
@article{CROSSINGTHECURL,
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	year = 2018,
	month = aug,
	adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	url = {https://arxiv.org/abs/1808.01531}
}
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@article{MNIST,
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@inproceedings{STL,
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@article{IMAGENET,
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@inproceedings{CELEBA,
	title = {Deep Learning Face Attributes in the Wild},
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	url = {https://arxiv.org/abs/1411.7766}
}
@misc{COLINTWEET,
	title = {PSA},
	author = {Raffel, Colin},
	year = {2018},
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@article{ACGAN,
	author = {Odena, A. and Olah, C. and Shlens, J.},
	title = "Conditional Image Synthesis With Auxiliary Classifier GANs",
	journal = {ArXiv e-prints},
	archiveprefix = "arXiv",
	eprint = {1610.09585},
	primaryclass = "stat.ML",
	keywords = {Statistics - Machine Learning, Computer Science - Computer Vision and Pattern Recognition},
	year = 2016,
	month = oct,
	url = {https://arxiv.org/abs/1610.09585},
	adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{PROGRESSIVEGAN,
	author = {Tero Karras and Timo Aila and Samuli Laine and Jaakko Lehtinen},
	title = {Progressive Growing of GANs for Improved Quality, Stability, and Variation},
	journal = {CoRR},
	volume = {abs/1710.10196},
	year = {2017},
	url = {http://arxiv.org/abs/1710.10196},
	archiveprefix = {arXiv},
	eprint = {1710.10196},
	timestamp = {Mon, 13 Aug 2018 16:46:42 +0200},
	biburl = {https://dblp.org/rec/bib/journals/corr/abs-1710-10196},
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}
@article{SAGAN,
	author = {Zhang, H. and Goodfellow, I. and Metaxas, D. and Odena, A.},
	title = "Self-Attention Generative Adversarial Networks",
	journal = {ArXiv e-prints},
	archiveprefix = "arXiv",
	eprint = {1805.08318},
	primaryclass = "stat.ML",
	keywords = {Statistics - Machine Learning, Computer Science - Machine Learning},
	year = 2018,
	month = may,
	url = {https://arxiv.org/abs/1805.08318},
	adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{GENERALIZATIONANDEQUILIBRIUM,
	author = {Sanjeev Arora and Rong Ge and Yingyu Liang and Tengyu Ma and Yi Zhang},
	title = {Generalization and Equilibrium in Generative Adversarial Nets (GANs)},
	journal = {CoRR},
	volume = {abs/1703.00573},
	year = {2017},
	url = {http://arxiv.org/abs/1703.00573},
	archiveprefix = {arXiv},
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@article{IMPROVEDTECHNIQUES,
	author = {Tim Salimans and Ian J. Goodfellow and Wojciech Zaremba and Vicki Cheung and Alec Radford and Xi Chen},
	title = {Improved Techniques for Training GANs},
	journal = {CoRR},
	volume = {abs/1606.03498},
	year = {2016},
	url = {http://arxiv.org/abs/1606.03498},
	archiveprefix = {arXiv},
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}
@article{FID,
	author = {Martin Heusel and Hubert Ramsauer and Thomas Unterthiner and Bernhard Nessler and G{\"{u}}nter Klambauer and Sepp Hochreiter},
	title = {GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium},
	journal = {CoRR},
	volume = {abs/1706.08500},
	year = {2017},
	url = {http://arxiv.org/abs/1706.08500},
	archiveprefix = {arXiv},
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	timestamp = {Mon, 13 Aug 2018 16:48:19 +0200},
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}
@article{CLASSIFIERTWOSAMPLE,
	author = {Lopez-Paz, D. and Oquab, M.},
	title = "Revisiting Classifier Two-Sample Tests",
	journal = {ArXiv e-prints},
	archiveprefix = "arXiv",
	eprint = {1610.06545},
	primaryclass = "stat.ML",
	keywords = {Statistics - Machine Learning},
	year = 2016,
	month = oct,
	adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	url = {https://arxiv.org/abs/1610.06545}
}
@article{CYCLEGAN,
	author = {Jun{-}Yan Zhu and Taesung Park and Phillip Isola and Alexei A. Efros},
	title = {Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks},
	journal = {CoRR},
	volume = {abs/1703.10593},
	year = {2017},
	url = {http://arxiv.org/abs/1703.10593},
	archiveprefix = {arXiv},
	eprint = {1703.10593},
	timestamp = {Mon, 13 Aug 2018 16:48:06 +0200},
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	bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{DOMAINADAPTATION,
	author = {Konstantinos Bousmalis and Nathan Silberman and David Dohan and Dumitru Erhan and Dilip Krishnan},
	title = {Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks},
	journal = {CoRR},
	volume = {abs/1612.05424},
	year = {2016},
	url = {http://arxiv.org/abs/1612.05424},
	archiveprefix = {arXiv},
	eprint = {1612.05424},
	timestamp = {Mon, 13 Aug 2018 16:49:07 +0200},
	biburl = {https://dblp.org/rec/bib/journals/corr/BousmalisSDEK16},
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}
@article{DEEPIMAGEPRIOR,
	author = {Dmitry Ulyanov and Andrea Vedaldi and Victor S. Lempitsky},
	title = {Deep Image Prior},
	journal = {CoRR},
	volume = {abs/1711.10925},
	year = {2017},
	url = {http://arxiv.org/abs/1711.10925},
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	biburl = {https://dblp.org/rec/bib/journals/corr/abs-1711-10925},
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}
@article{SEQGAN,
	author = {Lantao Yu and Weinan Zhang and Jun Wang and Yong Yu},
	title = {SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient},
	journal = {CoRR},
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}
@article{LOSSSURFACE,
	author = {Anna Choromanska and Mikael Henaff and Micha{\"{e}}l Mathieu and G{\'{e}}rard Ben Arous and Yann LeCun},
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	journal = {CoRR},
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	biburl = {https://dblp.org/rec/bib/journals/corr/ChoromanskaHMAL14},
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}
@misc{LGQGAN,
	title = {Understanding GANs: the LQG Setting},
	author = {Soheil Feizi and Changho Suh and Fei Xia and David Tse},
	year = {2018},
	url = {https://openreview.net/forum?id=r1CE9GWR-}
}
@article{SGA,
	author = {David Balduzzi and S{\'{e}}bastien Racaniere and James Martens and Jakob Foerster and Karl Tuyls and Thore Graepel},
	title = {The Mechanics of n-Player Differentiable Games},
	journal = {CoRR},
	volume = {abs/1802.05642},
	year = {2018},
	url = {http://arxiv.org/abs/1802.05642},
	archiveprefix = {arXiv},
	eprint = {1802.05642},
	timestamp = {Mon, 13 Aug 2018 16:46:44 +0200},
	biburl = {https://dblp.org/rec/bib/journals/corr/abs-1802-05642},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{GANGS,
	title = {GANGs: Generative Adversarial Network Games},
	author = {Oliehoek, Frans A and Savani, Rahul and Gallego-Posada, Jose and Van der Pol, Elise and De Jong, Edwin D and Gro{\ss}, Roderich},
	journal = {arXiv preprint arXiv:1712.00679},
	year = {2017},
	url = {https://arxiv.org/abs/1712.00679}
}
@article{BEYONDLOCAL,
	author = {Oliehoek, F.~A. and Savani, R. and Gallego, J. and van der Pol, E. and Gross, R.},
	title = "Beyond Local Nash Equilibria for Adversarial Networks",
	journal = {ArXiv e-prints},
	archiveprefix = "arXiv",
	eprint = {1806.07268},
	keywords = {Computer Science - Machine Learning, Computer Science - Computer Science and Game Theory, Statistics - Machine Learning},
	year = 2018,
	month = jun,
	adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	url = {https://arxiv.org/abs/1806.07268v2}
}
@article{CHEKHOV,
	author = {Paulina Grnarova and Kfir Y. Levy and Aur{\'{e}}lien Lucchi and Thomas Hofmann and Andreas Krause},
	title = {An Online Learning Approach to Generative Adversarial Networks},
	journal = {CoRR},
	volume = {abs/1706.03269},
	year = {2017},
	url = {http://arxiv.org/abs/1706.03269},
	archiveprefix = {arXiv},
	eprint = {1706.03269},
	timestamp = {Mon, 13 Aug 2018 16:49:15 +0200},
	biburl = {https://dblp.org/rec/bib/journals/corr/GrnarovaLLHK17},
	bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{IMAGENET1HOUR,
	author = {Priya Goyal and Piotr Dollar and Ross B. Girshick and Pieter Noordhuis and Lukasz Wesolowski and Aapo Kyrola and Andrew Tulloch and Yangqing Jia and Kaiming He},
	title = {Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour},
	journal = {CoRR},
	volume = {abs/1706.02677},
	year = {2017},
	url = {http://arxiv.org/abs/1706.02677},
	archiveprefix = {arXiv},
	eprint = {1706.02677},
	timestamp = {Mon, 13 Aug 2018 16:49:10 +0200},
	biburl = {https://dblp.org/rec/bib/journals/corr/GoyalDGNWKTJH17},
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}
@inproceedings{TPU,
	title = {In-datacenter performance analysis of a tensor processing unit},
	author = {Jouppi, Norman P and Young, Cliff and Patil, Nishant and Patterson, David and Agrawal, Gaurav and Bajwa, Raminder and Bates, Sarah and Bhatia, Suresh and Boden, Nan and Borchers, Al and others},
	booktitle = {Computer Architecture (ISCA), 2017 ACM/IEEE 44th Annual International Symposium on},
	pages = {1--12},
	year = {2017},
	organization = {IEEE},
	url = {https://arxiv.org/abs/1704.04760}
}
@article{BIGGAN,
	author = {Brock, A. and Donahue, J. and Simonyan, K.},
	title = "Large Scale GAN Training for High Fidelity Natural Image Synthesis",
	journal = {ArXiv e-prints},
	archiveprefix = "arXiv",
	eprint = {1809.11096},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	year = 2018,
	month = sep,
	url = {https://arxiv.org/abs/1809.11096},
	adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@article{SPECTRALNORM,
	author = {Takeru Miyato and Toshiki Kataoka and Masanori Koyama and Yuichi Yoshida},
	title = {Spectral Normalization for Generative Adversarial Networks},
	journal = {CoRR},
	volume = {abs/1802.05957},
	year = {2018},
	url = {http://arxiv.org/abs/1802.05957},
	archiveprefix = {arXiv},
	eprint = {1802.05957},
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	biburl = {https://dblp.org/rec/bib/journals/corr/abs-1802-05957},
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}
@article{PIXELRNN,
	author = {A{\"{a}}ron van den Oord and Nal Kalchbrenner and Koray Kavukcuoglu},
	title = {Pixel Recurrent Neural Networks},
	journal = {CoRR},
	volume = {abs/1601.06759},
	year = {2016},
	url = {http://arxiv.org/abs/1601.06759},
	archiveprefix = {arXiv},
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@article{PIXELCNN,
	author = {A{\"{a}}ron van den Oord and Nal Kalchbrenner and Oriol Vinyals and Lasse Espeholt and Alex Graves and Koray Kavukcuoglu},
	title = {Conditional Image Generation with PixelCNN Decoders},
	journal = {CoRR},
	volume = {abs/1606.05328},
	year = {2016},
	url = {http://arxiv.org/abs/1606.05328},
	archiveprefix = {arXiv},
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	biburl = {https://dblp.org/rec/bib/journals/corr/OordKVEGK16},
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@article{PIXELCNNPP,
	author = {Tim Salimans and Andrej Karpathy and Xi Chen and Diederik P. Kingma},
	title = {PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications},
	journal = {CoRR},
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	year = {2017},
	url = {http://arxiv.org/abs/1701.05517},
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	bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{WAVENET

repository: ludwigschubert/rollup-plugin-bibtex

repo_name: ludwigschubert/rollup-plugin-bibtex

gha_license_id: MIT

gha_language: TeX