Graphics

High-resolution Deep Convolutional Generative Adversarial Networks.

News: Since its first appearance in 2017, this work has attracted worldwide attention as one of the first techniques to generate AI artwork. The pieces have been showed in major NFT showcases with public applaud and following. Visit a VR display.

Link to Curtó & Zarza. Preview.

Alternate Link 1 to Curtó & Zarza.

Alternate Link 2 to Curtó & Zarza.

For more information visit the website:

www.decurto.tw

If you use Curtó & Zarza in a publication, please cite the paper below:

    @article{Curto17_2,
      author = "J. D. Curt\'o and I. C. Zarza and F. Torre and I. King and M. R. Lyu",
      title = "High-resolution Deep Convolutional Generative Adversarial Networks",
      journal = "arXiv:1711.06491",
      year = "2017",
    }

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Change Log

Version 1.0, released on 24/01/2019.

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File Information

• Samples (graphics/samples/)**.
  • 14,248 cropped faces. Balanced in terms of ethnicity. Mirror images included to enhance pose variation.
• Labels (labels/c&z.csv and labels/c&z.p).
  • CSV file with attribute information: Filename, Age, Ethnicity, Eyes Color, Facial Hair, Gender, Glasses, Hair Color, Hair Covered, Hair Style, Smile and Visible Forehead. We also include format Pickle to load in Python.
• Code (script_tensorflow/classification.py and generate_subfolders.py).
  • Script to do classification using Tensorflow.
  • Script to generate adequate subfolder of specific attribute. Useful to load into frameworks of Machine Learning.
• HDCGAN Synthetic Images (graphics/hdcgan/).
  • 4,239 faces generated by HDCGAN trained on CelebA. Resized at 128x128.
• Additional Images (graphics/extra/samples/, labels/extra_c&z.csv and labels/extra_c&z.p)**.
  • 3,384 cropped faces with labels. Ethnicity: White.

** Please note that we do not own the copyrights to these images. Their use is RESTRICTED to non-commercial research and educational purposes.