Skip to content

Code and notebooks related to the paper: "Reconstructing Faces from fMRI Patterns using Deep Generative Neural Networks" by VanRullen & Reddy, 2019

License

Notifications You must be signed in to change notification settings

rufinv/VAE-GAN-CelebA

Repository files navigation

VAE-GAN-CelebA

Python code related to the paper: "Reconstructing Faces from fMRI Patterns using Deep Generative Neural Networks" by VanRullen & Reddy (2019)

This folder contains:

  • a link to a pre-trained VAE-GAN model checkpoint 'vaegan_celeba.ckpt' (~15 epochs on CelebA dataset=50,000 batches of 64 images)
  • a set of .py functions for the VAE-GAN face decomposition/reconstruction model, in particular:
    • VAEGAN_image2latent.py => goes from any image file to the corresponding 1024D latent encoding (saved as a Matlab .mat file)
    • VAEGAN_latent2image.py => goes from a 1024D latent encoding (Matlab .mat file) to the corresponding image(s)
  • (optional) a link to download the fMRI datasets (4 subjects, each saw > 8,000 faces in the scanner) and some Matlab analysis code

Example usage:

VAEGAN_image2latent.py -i example.jpg     #this will create example_z.mat with the 1024 latent vars
VAEGAN_latent2image.py -i example_z.mat   #this will generate example_z_g.jpg (and also example_z_g.mat)

Requirements:

  • Python >= 3.4
  • Tensorflow >= 1.8
  • matplotlib, numpy, scipy, skimage

About

Code and notebooks related to the paper: "Reconstructing Faces from fMRI Patterns using Deep Generative Neural Networks" by VanRullen & Reddy, 2019

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages