Repository for our ICLR 2018 paper: memoryGAN
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checkpoint/pretrained_model
models
.gitignore
LICENSE
README.md
affmnist.py
chair.py
cifar10.py
datasets.py
fashion.py
ops.py
requirements.txt
run.py
utils.py

README.md

MemoryGAN

Code for our paper Memorization Precedes Generation: Learning Unsupervised GANs with Memory Networks by Youngjin Kim, Minjung Kim, Gunhee Kim. This repository includes codes for training and testing MemoryGAN with Fashion-MNIST, affine-transformed MNIST and CIFAR10 datasets. It also include model parameters of MemoryGAN that we trained with CIFAR10 dataset. If you use this in your research, we kindly ask that you cite the below ICLR 2018 paper.

@inproceedings{
	kim2018memorization,
	title={Memorization Precedes Generation: Learning 	Unsupervised {GAN}s with Memory Networks},
	author={Youngjin Kim and Minjung Kim and Gunhee Kim},
	booktitle={International Conference on Learning Representations},
	year={2018},
	url={https://openreview.net/forum?id=rkO3uTkAZ},
}

Dependencies

  • python 2
  • tensorflow 1.4

Install python packages

pip install -r requirements.txt

How to use

Downloading datasets

  • Once you run the trainig script, it will automatically download and places datasets into dataset folder.
  • See model/train.py, affmnist.py, fashion.py and cifar10.py for more details.

Training

  • Run run.py with arguments. For examples, run one of following commands.
python run.py --dataset=fashion --lr_decay=False --use_augmentation=False
python run.py --dataset=affmnist --lr_decay=False --use_augmentation=False
python run.py --dataset=cifar10 --lr_decay=True --use_augmentation=True
  • See more arguments and hyperparameter settings in run.py and models/config.py

Running pretrained model (CIFAR10)

  • Run run.py with --load_cp_dir and --is_train arguments
python run.py --dataset=cifar10 --is_train=False --load_cp_dir=checkpoint/pretrained_model

License

MIT License. Please see the LICENSE file for details.