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.

GAN python scholarship

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Paul Corbalan b0231c2fba feat(dataset): Import raw data import script		2024-01-23 15:59:37 +01:00
.devcontainer	feat(devcontainer): Remove ipykernel for Jupyter Notebook	2024-01-23 15:01:47 +01:00
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models	Remove dots from module names to execute the code with pytorch 1.0.0.	2018-12-25 22:59:23 +01:00
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clustering.py	feat(dataset): Import raw data import script	2024-01-23 15:59:37 +01:00
generate.py	Save each image in its own file.	2018-12-26 16:03:28 +01:00
main.py	Move options to main	2024-01-09 23:06:55 +01:00
requirements.txt	Add torchviz and notebook for visualisation	2024-01-23 14:51:02 +01:00

README.md

Wasserstein GAN

This project is based on the following resources:

Paper: Wasserstein GAN
GitHub: martinarjovsky/WassersteinGAN

Use

python main.py