Keras VAEs and GANs

Keras implementation of various deep generative networks such as VAE and GAN.

Models

Standard models

Variational autoencoder (VAE) [Kingma et al. 2013]
Generative adversarial network (GAN or DCGAN) [Goodfellow et al. 2014]
Improved GAN [Salimans et al. 2016]
Energy-based GAN (EBGAN) [Zhao et al. 2016]
Adversarially learned inference (ALI) [Dumoulin et al. 2017]

Conditional models

Conditional variational autoencoder [Kingma et al. 2014]
CVAE-GAN [Bao et al. 2017]

Usage

Prepare datasets

First, download img_align_celeba.zip and list_attr_celeba.txt from CelebA webpage. Then, place these files to datasets and run create_database.py on databsets directory.

Training

# Standard models
python train.py --model=dcgan --epoch=200 --batchsize=100 --output=output

# Conditional models
python train_conditional.py --model=cvaegan --epoch=200 --batchsize=100 --output=output

References

Kingma et al., "Auto-Encoding Variational Bayes", arXiv preprint 2013.
Goodfellow et al., "Generative adversarial nets", NIPS 2014.
Salimans et al., "Improved Techniques for Training GANs", arXiv preprint 2016.
Zhao et al., "Energy-based generative adversarial network", arXiv preprint 2016.
Dumoulin et al. "Adversarially learned inference", ICLR 2017.
Kingma et al., "Semi-supervised learning with deep generative models", NIPS 2014.
Bao et al., "CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training", arXiv preprint 2017.

alexrakowski / keras-generative