gansformer-reproducibility-challenge

Project for Advance Topic in Machine Learning course @ USI 21/22.
See https://github.com/GiorgiaAuroraAdorni/gansformer-reproducibility-challenge, https://drive.google.com/drive/folders/1sqHD-X4mLOOkoT-xJvWGdPlwxb5et0kA?usp=sharing for datasets and https://drive.google.com/drive/folders/1ZFfO4HVINH-aDQbgLscJxNTqGLEIOMZv?usp=sharing for models.

Contributors

Giorgia Adorni — giorgia.adorni@usi.ch GiorgiaAuroraAdorni

Felix Boelter — felix.boelter@usi.ch felixboelter

Stefano Carlo Lambertenghi — stefano.carlo.lambertenghi@usi.ch steflamb

Prerequisites

• Python 3
• Tensorflow 1.X

Installation

Clone our repository and install the requirements

$ git clone https://github.com/GiorgiaAuroraAdorni/gansformer-reproducibility-challenge
$ cd gansformer-reproducibility-challenge/src
$ pip install -r requirements.txt

Usage

For the usage, go to the colab notebooks directory:

• Run Reproducibility_model_trainer.ipynb for training the models: Stylegan2, GANformers with Simplex and Duplex Attention and GANformers with Simplex and Duplex Attention (with vanilla StyleGAN2 discriminator).
• Run Reproducibility_result_visualizer.ipynb for the visualisation phase: here you can select the model that you want to use and generate random images, perform a symple interpolation of the latent space or even perform style mixing starting from a chosen target image.