Project was created during participation in the Krakow Applied Physicsand Computer Science Summer School ’20. The aim of the project was to test the latest GenerativeAdversarial Network (GAN) models for their application in simulating physical events;
- Michał Kacprzak;
- Paweł Kopciewicz as supervisor;
- Python 3.7
$ git clone https://github.com/MichalKacprzak99/reconstruction_particle_mass_spectra
$ cd reconstruction_particle_mass_spectra
$ sudo pip3 install -r requirements.txt
- Open files/main.py.
- Create object of GAN.
- Call class method "train".
from GAN.gan import GAN
if __name__ == '__main__':
gan = GAN()
gan.train(30000)
Implementation of Boundary-Seeking Generative Adversarial Networks.
Paper: https://arxiv.org/abs/1702.08431
Implementation of DualGAN: Unsupervised Dual Learning for Image-to-Image Translation.
Paper: https://arxiv.org/abs/1704.02510
Implementation of Generative Adversarial Network with a MLP generator and discriminator.
Paper: https://arxiv.org/abs/1406.2661
Implementation of Wasserstein GAN (with DCGAN generator and discriminator).