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 |

Inception scores are calculated by average of 10 evaluation with 5000 samples.

FIDs are calculated with 50000 train dataset and 10000 generated samples.