TwiggyDaniels / GAN_Convergence

A quick review and experiment of the (lack of) theoretical convergence guarantees of Generative Adverserial Networks. Then the Wasserstein Distance as remediation.

GAN_Convergence

This notebook contains an experiment to empirically compare the convergence of a standard GAN with that of a Wasserstein GAN of identical architecture (with the exception of outputs). The models attempt to generate greyscale images utilizing the CelebA dataset.

Installing

Install the necessary packages

pip3 install -r requirements.txt

TODO

• Citations for CelebA dataset
• Embed some images of the generated faces
• Clean up directory structure
• Split the actual model into a .py file instead of a class within the notebook
• Re-edit the latex report and upload it
• Improve project description