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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 Graphics

Alternate Link to Graphics

For more information visit the website:

If you use Graphics in a publication, please cite the paper below:

      author = "J. D. Curt\'o and H. C. Zarza and T. Kim",
      title = "High-resolution Deep Convolutional Generative Adversarial Networks",
      journal = "arXiv:1711.06491",
      year = "2019",

Change Log

Version 1.0, released on 24/01/2019

File Information

  • Images (graphics/samples/)**
    • 14,248 cropped face images. 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 Pickle format to load in Python.
  • Code (scripts_tensorflow/ and
    • Script to do classification benchmarks 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 face images 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 face images 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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