yhlleo / DWC-GAN

DWC-GAN, ACM MM 2020.

DWC-GAN

Describe What to Change: A Text-guided Unsupervised Image-to-Image Translation Approach, accepted to ACM International Conference on Multimedia(ACM MM), 2020. [Paper]|[arXiv]|[code]

Configuration

See the environment.yaml. We provide an user-friendly configuring method via Conda system, and you can create a new Conda environment using the command:

conda env create -f environment.yaml

CelebA faces

• Official homepage of dataset: link
• Prepare the dataset as the bellow structure:

datasets
  |__celeba
       |__images
       |    |__xxx.jpg
       |    |__...
       |__list_attr_celeba.txt

Pretrained Models

• CelebA: google drive (coming soon)

Training & Testing

• Train:

sh ./scripts/train_celeba_faces.sh <gpu_id> 0

Evaluation codes

We evaluate the performances of the compared models mainly based on this repo: GAN-Metrics

References

If our project is useful for you, please cite our papers:

@inproceedings{liu2020describe,
  title={Describe What to Change: A Text-guided Unsupervised Image-to-Image Translation Approach},
  author={Liu, Yahui and De Nadai, Marco and Cai, Deng and Li, Huayang and Alameda-Pineda, Xavier and Sebe, Nicu and Lepri, Bruno},
  booktitle={Proceedings of the 28th ACM International Conference on Multimedia},
  year={2020}
}