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

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).

kristofe/gan_bound