generative-models-in-tensorflow

a collection of generative adversarial networks implemented in TensorFlow

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Models

U-GAT-IT: (Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation)

StyleGAN: (A Style-Based Generator Architecture for Generative Adversarial Networks)

CAN: (Creative Adversarial Networks Generating "Art" by Learning About Styles and Deviating from Style Norms)

Citations

@misc{kim2019ugatit,
    title={U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation},
    author={Junho Kim and Minjae Kim and Hyeonwoo Kang and Kwanghee Lee},
    year={2019},
    eprint={1907.10830},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

@inproceedings{karras2019style,
  title={A style-based generator architecture for generative adversarial networks},
  author={Karras, Tero and Laine, Samuli and Aila, Timo},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={4401--4410},
  year={2019}
}

@misc{2017cans,
  author = {Phillip Kravtsov and Phillip Kuznetsov},
  title = {Creative Adversarial Networks},
  year = {2017},
  howpublished = {\url{https://github.com/mlberkeley/Creative-Adversarial-Networks}},
  note = {commit xxxxxxx}
}