Minimal Wasserstein GAN
This is a simple TensorFlow implementation of Wasserstein Generative Advesarial Networks applied to MNIST.
Some example generated digits:
How to run
Simply run the file
wgan_mnist.py. Results will be displayed in real time, while full training takes about an hour using a GPU.
The implementation follows Improved Training of Wasserstein GANs, using the network from the accompanying code. In particular both the generator and discriminator uses 3 convolutional layers with 5x5 convolutions.