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README.md
data_loader.py
main.py
models.py
params.py
utils.py

README.md

WGAN-GP (Wasserstein GANs with Gradient Penalty)

This script implements the paper Improved Training of Wasserstein GANs.

Usage

Run python3 main.py to start training WGAN-GP.

Structure

  • data_loader.py: dataset setting and data loader.
  • models.py: discriminator and generator network model.
  • params.py: parameters for the training process.
  • utils.py: some useful fuctions for training.

Result

Samples from CIFAR dataset:

WGAN-real_samples

Samples from generator (50000 iters):

WGAN-GP_fake_image-50000