jrtechs-computer-vision-GANs-paper/proposal.bib

@PREAMBLE{
 "\providecommand{\noopsort}[1]{}" 
 # "\providecommand{\singleletter}[1]{#1}%" 
}

@misc{goodfellow2014generative,
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    eprint={1406.2661},
    archivePrefix={arXiv},
    primaryClass={stat.ML}
}

@misc{arjovsky2017wasserstein,
    title={Wasserstein GAN},
    author={Martin Arjovsky and Soumith Chintala and Léon Bottou},
    year={2017},
    eprint={1701.07875},
    archivePrefix={arXiv},
    primaryClass={stat.ML}
}


@misc{radford2015unsupervised,
    title={Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks},
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    year={2015},
    eprint={1511.06434},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

@article{widrow1962generalization,
  title={Generalization and Information Storage in Networks of ADALINE Neurons. Self Organizing Systems},
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  year={1962}
}


@ARTICLE{overviewDocument,
    author={Z. {Pan} and W. {Yu} and X. {Yi} and A. {Khan} and F. {Yuan} and Y. {Zheng}},
    journal={IEEE Access},
    title={Recent Progress on Generative Adversarial Networks (GANs): A Survey},
    year={2019},
    volume={7},
    number={},
    pages={36322-36333},
    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},
    doi={10.1109/ACCESS.2019.2905015},
    ISSN={2169-3536},
month={},}


@article{cGAN,
  author    = {Mehdi Mirza and
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  journal   = {CoRR},
  volume    = {abs/1411.1784},
  year      = {2014},
  url       = {http://arxiv.org/abs/1411.1784},
  archivePrefix = {arXiv},
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}

@article{lsgan,
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               Qing Li and
               Haoran Xie and
               Raymond Y. K. Lau and
               Zhen Wang},
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  journal   = {CoRR},
  volume    = {abs/1611.04076},
  year      = {2016},
  url       = {http://arxiv.org/abs/1611.04076},
  archivePrefix = {arXiv},
  eprint    = {1611.04076},
  timestamp = {Wed, 13 Nov 2019 15:48:57 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/MaoLXLW16.bib},
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}

@inproceedings{acgan,
author = {Odena, Augustus and Olah, Christopher and Shlens, Jonathon},
title = {Conditional Image Synthesis with Auxiliary Classifier GANs},
year = {2017},
publisher = {JMLR.org},
booktitle = {Proceedings of the 34th International Conference on Machine Learning - Volume 70},
pages = {2642–2651},
numpages = {10},
location = {Sydney, NSW, Australia},
series = {ICML’17}
}

@article{infogan,
  author    = {Xi Chen and
               Yan Duan and
               Rein Houthooft and
               John Schulman and
               Ilya Sutskever and
               Pieter Abbeel},
  title     = {InfoGAN: Interpretable Representation Learning by Information Maximizing
               Generative Adversarial Nets},
  journal   = {CoRR},
  volume    = {abs/1606.03657},
  year      = {2016},
  url       = {http://arxiv.org/abs/1606.03657},
  archivePrefix = {arXiv},
  eprint    = {1606.03657},
  timestamp = {Mon, 03 Sep 2018 12:15:29 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/ChenDHSSA16.bib},
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@article{pytorch,
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@book{generalDeepLearning,
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}