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121 lines
4.1 KiB
121 lines
4.1 KiB
@PREAMBLE{
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"\providecommand{\noopsort}[1]{}"
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# "\providecommand{\singleletter}[1]{#1}%"
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}
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@misc{goodfellow2014generative,
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title={Generative Adversarial Networks},
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author={Ian J. Goodfellow and Jean Pouget-Abadie and Mehdi Mirza and Bing Xu and David Warde-Farley and Sherjil Ozair and Aaron Courville and Yoshua Bengio},
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year={2014},
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eprint={1406.2661},
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archivePrefix={arXiv},
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primaryClass={stat.ML}
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}
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@misc{arjovsky2017wasserstein,
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title={Wasserstein GAN},
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author={Martin Arjovsky and Soumith Chintala and Léon Bottou},
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year={2017},
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eprint={1701.07875},
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archivePrefix={arXiv},
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primaryClass={stat.ML}
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}
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@misc{radford2015unsupervised,
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title={Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks},
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author={Alec Radford and Luke Metz and Soumith Chintala},
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year={2015},
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eprint={1511.06434},
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archivePrefix={arXiv},
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primaryClass={cs.LG}
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}
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@article{widrow1962generalization,
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title={Generalization and Information Storage in Networks of ADALINE Neurons. Self Organizing Systems},
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author={Widrow, Bernard},
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journal={Yovitz, MC, Jacobi, GT, and Goldstein, GD editors},
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pages={435--461},
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year={1962}
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}
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@ARTICLE{overviewDocument,
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author={Z. {Pan} and W. {Yu} and X. {Yi} and A. {Khan} and F. {Yuan} and Y. {Zheng}},
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journal={IEEE Access},
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title={Recent Progress on Generative Adversarial Networks (GANs): A Survey},
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year={2019},
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volume={7},
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number={},
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pages={36322-36333},
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keywords={artificial intelligence;neural nets;generative adversarial network;GANs;generative models;data generation capacity;artificial intelligence;Gallium nitride;Generators;Generative adversarial networks;Training;Feature extraction;Data models;Unsupervised learning;Deep learning;machine learning;unsupervised learning;generative adversarial networks},
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doi={10.1109/ACCESS.2019.2905015},
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ISSN={2169-3536},
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month={},}
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@article{cGAN,
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author = {Mehdi Mirza and
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Simon Osindero},
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title = {Conditional Generative Adversarial Nets},
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journal = {CoRR},
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volume = {abs/1411.1784},
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year = {2014},
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url = {http://arxiv.org/abs/1411.1784},
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archivePrefix = {arXiv},
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eprint = {1411.1784},
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timestamp = {Mon, 13 Aug 2018 16:48:15 +0200},
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biburl = {https://dblp.org/rec/journals/corr/MirzaO14.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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@article{lsgan,
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author = {Xudong Mao and
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Qing Li and
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Haoran Xie and
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Raymond Y. K. Lau and
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Zhen Wang},
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title = {Multi-class Generative Adversarial Networks with the {L2} Loss Function},
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journal = {CoRR},
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volume = {abs/1611.04076},
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year = {2016},
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url = {http://arxiv.org/abs/1611.04076},
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archivePrefix = {arXiv},
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eprint = {1611.04076},
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timestamp = {Wed, 13 Nov 2019 15:48:57 +0100},
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biburl = {https://dblp.org/rec/journals/corr/MaoLXLW16.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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@inproceedings{acgan,
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author = {Odena, Augustus and Olah, Christopher and Shlens, Jonathon},
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title = {Conditional Image Synthesis with Auxiliary Classifier GANs},
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year = {2017},
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publisher = {JMLR.org},
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booktitle = {Proceedings of the 34th International Conference on Machine Learning - Volume 70},
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pages = {2642–2651},
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numpages = {10},
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location = {Sydney, NSW, Australia},
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series = {ICML’17}
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}
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@article{infogan,
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author = {Xi Chen and
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Yan Duan and
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Rein Houthooft and
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John Schulman and
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Ilya Sutskever and
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Pieter Abbeel},
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title = {InfoGAN: Interpretable Representation Learning by Information Maximizing
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Generative Adversarial Nets},
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journal = {CoRR},
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volume = {abs/1606.03657},
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year = {2016},
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url = {http://arxiv.org/abs/1606.03657},
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archivePrefix = {arXiv},
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eprint = {1606.03657},
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timestamp = {Mon, 03 Sep 2018 12:15:29 +0200},
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biburl = {https://dblp.org/rec/journals/corr/ChenDHSSA16.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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