olirsn / keras-generative

Deep generative networks, coded with Keras.

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.