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improved_wgan_training

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Code for reproducing experiments in "Improved Training of Wasserstein GANs"

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Improved Training of Wasserstein GANs
=====================================
This is a project test Wasserstein GAN objectives on single image super-resolution. The code is built on a fork of [the popular project under the same title](https://github.com/igul222/improved_wgan_training).

We expand the original repo by another model
`gan_SR.py`: GANs that generate 64x64-pixel images from 16x16-low-resolution inputs. The default dataset is the cropped aligned celebA dataset. Other dataset would require minor modifications.

Data preprocessing is required before training:
> `bash celebA_preprocess.sh`

will download celebA dataset, crop and resize to 64x64. Note this might take a while.

To run, modify the path of data directory, summary directory (for tensorboard) and output directory in `run_script.sh`.

> `bash run_script.sh`

Here is a sample output, using WGAN-GP objective and DCGAN architecture and 1 epoch of training.  From left to right are low resolution input, bicubic interpolation, wgangp output and original image.

![input | bicubic | WGAN-gp | original](https://raw.githubusercontent.com/YuguangTong/improved_wgan_training/master/wgangp_dcgan_panel.png)

Description of the original repository
======================================
Code for reproducing experiments in ["Improved Training of Wasserstein GANs"](https://arxiv.org/abs/1704.00028).

## Prerequisites

- Python, NumPy, TensorFlow, SciPy, Matplotlib
- A recent NVIDIA GPU

## Models

Configuration for all models is specified in a list of constants at the top of
the file. Two models should work "out of the box":

- `python gan_toy.py`: Toy datasets (8 Gaussians, 25 Gaussians, Swiss Roll). 
- `python gan_mnist.py`: MNIST

For the other models, edit the file to specify the path to the dataset in
`DATA_DIR` before running. Each model's dataset is publicly available; the
download URL is in the file.

- `python gan_64x64.py`: 64x64 architectures (this code trains on ImageNet instead of LSUN bedrooms in the paper)
- `python gan_language.py`: Character-level language model
- `python gan_cifar.py`: CIFAR-10

improved_wgan_training

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