MMD-GAN

Code accompanying the paper MMD-GAN: Towards Deeper Understanding of Moment Matching Network.

Prerequisites

- Python, NumPy, Scipy
- PyTorch (v0.1.12)
- A recent Nvidia GPU

Usage

./mmd_gan [OPTIONS]
OPTIONS:
    --dataset DATASET: type of dataset (mnist/cifar10/celeba/lsun)
    --dataroot DATAROOT: path to dataset
    --workers WORKERS: number of threads to load data
    --batch_size BATCH_SIZE: batch size for training
    --image_size IMAGE_SIZE: image size of dataset
    --nc NC: number of channels in images
    --nz NZ: hidden dimension in z and codespace
    --max_iter MAX_ITER: max iteration for training
    --lr LR: learning rate (default 5e-5)
    --gpu_device GPU_DEVICE: gpu id (default 0)
    --netG NETG: path to generator model
    --netD NETD: path to discriminator model
    --Diters DITERS: number of updates for discriminator per one generator update
    --experiment EXPERIMENT: output directory of sampled images

For a quick start, please set the DATA_PATH variable in run_exp.sh to

    ./data

and run

    $ ./run_exp.sh [mnist/cifar10/celeba/lsun]

Dataset

For mnist and cifar10, the dataset will be automatically download if not exist in
the designated DATAROOT directory.

For CelebA and LSUN dataset, please run the download script in ./data directory.

More Info

This repository is by
Chun-Liang Li,
Wei-Cheng Chang,
Yu Cheng,
Yiming Yang,
Barnabás Póczos,
and contains the source code to
reproduce the experiments in our paper
MMD GAN: Towards Deeper Understanding of Moment Matching Network.
If you find this repository helpful in your publications, please consider citing our paper.

@article{li2017mmd,
    title={MMD GAN: Towards Deeper Understanding of Moment Matching Network},
    author={Li, Chun-Liang and Chang, Wei-Cheng and Cheng, Yu and Yang, Yiming and P{\'o}czos, Barnab{\'a}s},
    journal={arXiv preprint arXiv:1705.08584},
    year={2017}
}

For any questions and comments, please send your email to
wchang2@cs.cmu.edu