Painter Impersonation using GANs

This repository contains notebooks and pythonf files for this kaggle competition.

The repository has the following structure:

.
├── DiffAugment.ipynb
├── cycleGAN.ipynb
├── dualdiscriminator.ipynb
├── requirements.txt
└── utils
    ├── cyclegan.py
    ├── diffaugmentation.py
    ├── dualdiscriminator.py
    ├── gan.py
    └── preprocessing.py

This code was developed on MacOS, Linux, and the Kaggle Jupyter notebok interface. The requirements for a MacOS with silicon chip are contained in the requirements.txt file. The Python version of the environment is 3.8.14.

In the Kaggle interface, we recomment using the TPU v3-8, for faster training computations.

In a nutshell

This repository is composed of two main parts, the notebooks and the python code in the utils folder. This folder is where the code is the most explained.

For this project, we have tested 3 methods of GANs. The initial goal of the Kaggle competition was to generate 7,000 Monet-esque pictures.

In order to do so, we have tested:

• a vanilla CycleGAN
• a vanilla CycleGAN + Differential Augmentation
• a vanilla CycleGAN + Differential Augmentation + Dual Headed Discriminator.

The theory and report for this project can be found here.