This repository contains an implementation of several Generative Adversarial Models such as:
- Conditional GANs
- Pix2Pix
- Cycle GANs
Conditional GANs were trained on the MNIST dataset and conditioned on the digits. The result is that after 8-9 epochs the results are very satisfying while after epoch 12 we start encountering worsening results which are probably due to vanishing gradient problems.
Trained to simulate the map->aerial photo application illustrated in the original paper.
Trained to simulate the photo->ukiyoe application illustrated in the paper. Therefore, identity loss is included by default in the config file.
In both cases the discriminator is a 70 x 70 PatchGAN discriminator while the generator is based on the U-Net architecture.
All parameters were chosen to simulate the results achieved in the paper.
In collaboration with: https://github.com/dbasso98
This implementation was the focus of the final project of the Deep Learning course held by Prof. Alessio Ansuini and Prof. Marco Zullich (DSSC). Here the link to the presentation's slides: GANs overview.