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Conditional Autoencoders with Adversarial Information Factorization

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Conditional Autoencoders with Adversarial Information Factorization

(New version of the code using ResNets will be provided soon to reproduce results in the latest verision of our paper: https://arxiv.org/pdf/1711.05175.pdf)

Updates to our paper and code (coming soon) include:

  1. Classification results on CelebA facial attributes
  2. Use of ResNets to improve image quality

To use code:

  1. Download the celebA dataset from http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
  2. Install dependancies listed in requirements.txt
  3. You will also need pyTorch which may be downloaded from http://pytorch.org
  4. Run the jupyter notebooks to get the data tensors xTrain.npy and yAllTrain.npy and move them in to folder celebA/InData/
  5. The code may be run from cmd line with various options detailed in the code

Example results:

alt text alt text

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