Collection of various GAN models using my own way of implementing them.
At this point four different generative models are implemented:
- Conditional Improved Wasserstein GAN (CWGAN-GP)
- Improved Wasserstein GAN (WGAN-GP)
- Boundary Equilibrium GAN (BEGAN)
- Deep Convolutional GAN (DCGAN)
- Variational AutoEncoder with learned similarity metric (WGAN-GP-VAE)
The WGAN-GP-VAE model implementation comes from my other project, as a part of my Master Thesis (AI)
The models folder contains all the generative models implemented.
To install the python environment for this project, refer to the Pipenv setup guide