Variational Autoencoder with Implicit Optimal Priors

This is a pytorch implementation of the following paper [AAAI] [arXiv]:

@inproceedings{takahashi2019variational,
  title={Variational Autoencoder with Implicit Optimal Priors},
  author={Takahashi, Hiroshi and Iwata, Tomoharu and Yamanaka, Yuki and Yamada, Masanori and Yagi, Satoshi},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={33},
  pages={5066--5073},
  year={2019}
}

Please read license.txt before reading or using the files.

Prerequisites

Please install python>=3.6, torch, torchvision, numpy and scipy.

Usage

usage: main.py [-h] [--dataset DATASET] [--prior PRIOR]
               [--learning_rate LEARNING_RATE] [--seed SEED]

• You can choose the dataset from following four image datasets: MNIST, OMNIGLOT, Histopathology and FreyFaces.
• You can choose the prior of the VAE from normal (standard Gaussian prior) or iop (implicit optimal prior).
• You can also change the random seed of the training and learning_rate of the optimizer (Adam).

Example

MNIST with standard Gaussian prior:

python main.py --dataset MNIST --prior normal

MNIST with implicit optimal prior:

python main.py --dataset MNIST --prior iop

Output

• After the training, the mean of log-likelihood for test dataset will be displayed.
• The detailed information of the training and test will be saved in npy directory.