GAN stability

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.

Usage

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

Notes

• 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.

• Batch normalization is currently not supported when using an exponential running average, as the running average is only computed over the parameters of the models and not the other buffers of the model.

Results

celebA-HQ

Imagenenet