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Graphics. High-resolution Deep Convolutional Generative Adversarial Networks.
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High-resolution Deep Convolutional Generative Adversarial Networks.

Link to Curtó & Zarza. Preview.

Alternate Link 1 to Curtó & Zarza.

Alternate Link 2 to Curtó & Zarza.

For more information visit the website:

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

      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",

Change Log

Version 1.0, released on 24/01/2019.

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/ and
    • 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.

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