This repository contains the experiments in the supplementary material for the paper Which Training Methods for GANs do actually Converge?.
To cite this work, please use
@INPROCEEDINGS{Mescheder2018ICML,
author = {Lars Mescheder and Sebastian Nowozin and Andreas Geiger},
title = {Which Training Methods for GANs do actually Converge?,
booktitle = {International Conference on Machine Learning (ICML)},
year = {2018}
}
You can find further details on our project page.
First download your data and put it into the ./data
folder.
To train a new model, first create a config script similar to the ones provided in the ./configs
folder. You can then train you model using
python train.py PATH_TO_CONFIG
To compute the inception score for your model and generate samples, use
python test.py PATH_TO_CONIFG
Finally, you can create nice latent space interpolations using
python interpolate.py PATH_TO_CONFIG
or
python interpolate_class.py PATH_TO_CONFIG
- For the results presented in the paper, we did not use a moving average over the weights. However, using a moving average helps to reduce noise and we therefore recommend its usage. Indeed, we found that using a moving average leads to much better inception scores on Imagenet.