VAE with a VampPrior

This is a PyTorch implementation of a new prior ("Variational Mixture of Posteriors" prior, or VampPrior for short) for the variational auto-encoder framework with one layer and two layers of stochastic hidden units as described in the following paper: * Jakub M. Tomczak, Max Welling, VAE with a VampPrior, arXiv preprint, 2017

Requirements

The code is compatible with: * pytorch 0.2.0

Data

The experiments can be run on the following datasets: * static MNIST: links to the datasets can found at link; * binary MNIST: the dataset is loaded from PyTorch; * OMNIGLOT: the dataset could be downloaded from link; * Caltech 101 Silhouettes: the dataset could be downloaded from link. * Frey Faces: the dataset could be downloaded from link. * Histopathology Gray: the dataset could be downloaded from link; * CIFAR 10: the dataset is loaded from PyTorch.

Run the experiment

1. Set-up your experiment in experiment.py.

2. Run experiment:

python experiment.py

Models

You can run a vanilla VAE, a one-layered VAE or a two-layered HVAE with the standard prior or the VampPrior by setting model_name argument to either: (i) vae or hvae_2level for MLP, (ii) convvae_2level for convnets, (iii) pixelhvae_2level for (ii) with a PixelCNN-based decoder, and specifying prior argument to either standard or vampprior.

Citation

Please cite our paper if you use this code in your research:

@article{TW:2017,
  title={{VAE with a VampPrior}},
  author={Tomczak, Jakub M and Welling, Max},
  journal={arXiv},
  year={2017}
}

Acknowledgments

The research conducted by Jakub M. Tomczak was funded by the European Commission within the Marie Skodowska-Curie Individual Fellowship (Grant No. 702666, Deep learning and Bayesian inference for medical imaging).