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_CONFIG

Finally, you can create nice latent space interpolations using

python interpolate.py PATH_TO_CONFIG

or

python interpolate_class.py PATH_TO_CONFIG

We also provide several pretrained models.

You can use the models for sampling by entering

python test.py PATH_TO_CONFIG

where PATH_TO_CONFIG is one of the config files

configs/pretrained/celebA_pretrained.yaml
configs/pretrained/celebAHQ_pretrained.yaml
configs/pretrained/imagenet_pretrained.yaml
configs/pretrained/lsun_bedroom_pretrained.yaml
configs/pretrained/lsun_bridge_pretrained.yaml
configs/pretrained/lsun_church_pretrained.yaml
configs/pretrained/lsun_tower_pretrained.yaml

Our script will automatically download the model checkpoints and run the generation. You can find the outputs in the output/pretrained folders. Similarly, you can use the scripts interpolate.py and interpolate_class.py for generating interpolations for the pretrained models.

Please note that the config files *_pretrained.yaml are only for generation, not for training new models: when these configs are used for training, the model will be trained from scratch, but during inference our code will still use the pretrained model.

• 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.