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ZUNIT

Pytorch implementation of our paper: "Zero-shot unsupervised image-to-image translation via exploiting semantic attributes".

Dependencies

you can install all the dependencies by

pip install -r requirements.txt

Datasets

  • Download CUB dataset.
  • Unzip the birds.zip at ./dataset.

Training

  • To view training results and loss plots, run
python -m visdom.server -p 8080

and click the URL http://localhost:8080.

  • Run
bash ./scripts/train_bird.sh

Testing

  • Run
bash ./scripts/test_bird.sh
  • The testing results will be saved in checkpoints/{exp_name}/results directory.

Results

Bibtex

If this work is useful for your research, please consider citing :

@article{CHEN2022104489,
title = {Zero-shot unsupervised image-to-image translation via exploiting semantic attributes},
journal = {Image and Vision Computing},
pages = {104489},
year = {2022},
issn = {0262-8856},
doi = {https://doi.org/10.1016/j.imavis.2022.104489},
author = {Yuanqi Chen and Xiaoming Yu and Shan Liu and Wei Gao and Ge Li}
}

Acknowledgement

The code used in this research is inspired by DMIT and FUNIT.

Contact

Feel free to contact me if there is any questions ([email protected]).