Awesome

Chainer-GAN-lib

This repository collects chainer implementation of state-of-the-art GAN algorithms.
These codes are evaluated with the inception score on Cifar-10 dataset.
Note that our codes are not faithful re-implementation of the original paper.

How to use

Install the requirements first:

pip install -r requirements.txt

This implementation has been tested with the following versions.

python 3.5.2
chainer 4.0.0
+ https://github.com/chainer/chainer/pull/3615
+ https://github.com/chainer/chainer/pull/3581
cupy 3.0.0
tensorflow 1.2.0 # only for downloading inception model
numpy 1.11.1

Download the inception score module forked from https://github.com/hvy/chainer-inception-score.

git submodule update -i

Download the inception model.

cd common/inception
python download.py --outfile inception_score.model

You can start training with train.py.

python train.py --gpu 0 --algorithm dcgan --out result_dcgan

Please see example.sh to train other algorithms.

Quantitative evaluation

	Inception	Inception (Official)	FID
Real data	12.0	11.24	3.2 (train vs test)
Progressive	8.5	8.8	19.1
SN-DCGAN	7.5	7.41	23.6
WGAN-GP	6.8	7.86 (ResNet)	28.2
DFM	7.3	7.72	30.1
Cramer GAN	6.4	-	32.7
DRAGAN	7.1	6.90	31.5
DCGAN-vanilla	6.7	6.16 [WGAN2] 6.99 [DRAGAN]	34.3
minibatch discrimination	7.0	6.86 (-L+HA)	31.3
BEGAN	5.4	5.62	84.0