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
More improved README in the works.
Description
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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