We are something of a painter ourselves

Team: Daniel Galperin, Jonas Hellgoth, Alexander Kunkel

This repository contains our final project for the lecture "Advanced Machine Learning" at the University of Heidelberg.

About

Using conditional invertible neural networks for image-to-image translation with landscape photos and Monet paintings. Our source code heavily draws on https://github.com/VLL-HD/conditional_INNs and the corresponding publication https://arxiv.org/abs/1907.02392.

Directories

All relevant python files for the final project can be found in the source folder:

config.py	hyperparameters and paths
data.py	load data from path specified in config.py
models.py	includes final architecture MonetCINN_squeeze
train.py & eval.py	train and evaluate models

You can safely ignore all other directories and files.

The model

The trained model used to generate all figures in the report can be downloaded here: https://drive.google.com/file/d/1obP2slgHca-HhP31gpaQIm5Qs374-4pT/view?usp=sharing

For loading, set appropriate paths in config.py.

The data

We were not sure if we are allowed to make the data sets public here. Therefore, please contact us if you wish to have access to the data we used.

Animations

You can find animations of images linearly interpolating between the reconstruction z and -z in latent space for 64 test images under 'test animations'.

Dependencies

All version numbers are only the minimum version required to run the code. Probably, most other versions will work too.

Package	Version
Pytorch	1.8.0
Numpy	1.19.5
Matplotlib	3.2.2
scikit-learn	0.22.2
PIL	0.1.12
albumentations	7.1.2
cv2	4.1.2