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keras-generative

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Deep generative networks, coded with Keras.

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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](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html).
Then, place these files to ``datasets`` and run ``create_database.py`` on ``databsets`` directory.

#### Training

```shell
# 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.

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