This project consisted in studying GANs in the case of Wasserstein distance, as part of the fifth-year course at INSA Toulouse in Applied Mathematics of High Dimensional and Deep Learning.

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README.md

Wasserstein GAN

Code accompanying the paper "Wasserstein GAN"

##Prerequisites

Computer with Linux or OSX
PyTorch
For training, an NVIDIA GPU is strongly recommended for speed. CPU is supported but training is very slow.

##Reproducing LSUN experiments

With DCGAN:

python main.py --dataset lsun --dataroot [lsun-train-folder] --cuda

With MLP:

python main.py --mlp_G --ngf 512