Awesome
Estimating the Success of Unsupervised Image to Image Translation
Pytorch implementation of "Estimating the Success of Unsupervised Image to Image Translation" (arxiv).
Prerequisites
- Python 2.7
- Pytorch
- Numpy/Scipy/Pandas
- Progressbar
- OpenCV
Download dataset
Download dataset [edges2shoes, edges2handbags, cityscapes, maps, facades]: bash datasets/download_pix2pix.sh $DATASET_NAME.
General GAN Bound (Alg.1 and Alg.2)
DiscoGAN: python ./discogan_arch/general_gan_bound_discogan.py --task_name=$DATASET_NAME
DistanceGAN: python ./discogan_arch/general_gan_bound_distancegan.py --task_name=$DATASET_NAME
Per Sample Bound (Alg.3)
Train G_1 model:
DiscoGAN: python ./discogan_arch/disco_gan_model.py --task_name=$DATASET_NAME --num_layers=3
DistanceGAN: python ./discogan_arch/general_gan_bound_distancegan.py --task_name=$DATASET_NAME
Then Train G_2:
DiscoGAN: python ./discogan_arch/gan_bound_per_sample_discogan.py --task_name=$DATASET_NAME --pretrained_generator_A_path='./models/model_gen_A-10' --pretrained_generator_B_path='./models/model_gen_B-10' --pretrained_discriminator_A_path='./models/model_dis_A-10' --pretrained_discriminator_B_path='./models/model_dis_B-10' --one_sample_index=$SAMPLE_NUMBER
DistanceGAN: python ./discogan_arch/gan_bound_per_sample_distancegan.py --task_name=$DATASET_NAME --pretrained_generator_A_path='./models/model_gen_A-10' --pretrained_generator_B_path='./models/model_gen_B-10' --pretrained_discriminator_A_path='./models/model_dis_A-10' --pretrained_discriminator_B_path='./models/model_dis_B-10' --one_sample_index=$SAMPLE_NUMBER
Options
Additional options can be found in ./discogan_arch/discogan_based_options/options.py
For specific configuration see DistanceGAN and DiscoGAN
Reference
If you found this code useful, please cite the following paper:
@inproceedings{Benaim2018EstimatingTS,
title={Estimating the Success of Unsupervised Image to Image Translation},
author={Sagie Benaim and Tomer Galanti and Lior Wolf},
booktitle={ECCV},
year={2018}
}
Acknowledgements
This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant ERC CoG 725974).