ArtGAN

Please visit https://github.com/cs-chan/ArtGAN for the final version.

Citation

This repository contains codes for the following paper (under review):

@article{tan2017learning,
  title={Learning a Generative Adversarial Network for High Resolution Artwork Synthesis},
  author={Tan, Wei Ren and Chan, Chee Seng and Aguirre, Hernan and Tanaka, Kiyoshi},
  journal={arXiv preprint arXiv:1708.09533},
  year={2017}
}

which is an extension to the following paper (ICIP 2017):

@article{tan2017artgan,
  title={ArtGAN: Artwork Synthesis with Conditional Categorial GANs},
  author={Tan, Wei Ren and Chan, Chee Seng and Aguirre, Hernan and Tanaka, Kiyoshi},
  journal={arXiv preprint arXiv:1702.03410},
  year={2017}
}

Prerequisites

• Python 2.7
• Tensorflow
• (Optional) Nervana's Systems neon
• (Optional) Nervana's Systems aeon

* Neon and aeon are required to load data. If other data loader is used, neon and aeon are not required. But, make sure that data format is 'NCHW'.

Trained models

Each link below is the best trained model for the corresponding dataset. Download and extract to 'models' folder:

• CIFAR-10 - http://www.cs-chan.com/source/ArtGAN/CIFAR64GANAE.zip

• STL-10 - http://www.cs-chan.com/source/ArtGAN/STL128GANAE.zip

• Flowers - http://www.cs-chan.com/source/ArtGAN/Flower128GANAE.zip

• CUB-200 - http://www.cs-chan.com/source/ArtGAN/CUB128GANAE.zip

• Wikiart Artist - http://www.cs-chan.com/source/ArtGAN/Artist128GANAE.zip

• Wikiart Genre - http://www.cs-chan.com/source/ArtGAN/Genre128GANAE.zip

• Wikiart Style - http://www.cs-chan.com/source/ArtGAN/Style128GANAE.zip

Feedback

Suggestions and opinions of this work (both positive and negative) are greatly welcome. Please contact the authors by sending email to Wei Ren Tan at wrtan.edu at gmail.com or Chee Seng Chan at cs.chan at um.edu.my

License

BSD-3, see LICENSE file for details.