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AGUIT: Attribute Guided Unpaired Image Translation

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Project | Paper

"Attribute Guided Unpaired Image-to-Image Translation with Semi-supervised Learning." Li, X., Hu, J., Zhang, S., Hong, X., Ye, Q., Wu, C., & Ji, R. arXiv preprint.

<img width="70%" src="./Figures/examples.png"/>

Merits

merits.

Usage

You need to use python3 to run this code. Other requirements are:

Besides, you need to format your dataset, like the ./datasets/CelebA_example. (the labeled folder and label.txt consist of the labeled images and their labels, and the unlabeled folder consists of the unlabeled image.)

Run the code with default configuration by (python train.py --config configs/face.yaml).

The outputs and checkpoint would be in ./outputs

And test your model by modify the code in test.py and (python test.py --config configs/face.yaml --checkpoint #your_checkpoint --input #your_input --output_dir #your_output_dir)

The gui-test-code is waiting for a final check, it would be more easy for you to test your model in the future. Please wait for couples of days.

Citation

If our paper helps your research, please cite it in your publications:

@article{li2019attribute,
  title={Attribute Guided Unpaired Image-to-Image Translation with Semi-supervised Learning},
  author={Li, Xinyang and Hu, Jie and Zhang, Shengchuan and Hong, Xiaopeng and Ye, Qixiang and Wu, Chenglin and Ji, Rongrong},
  journal={arXiv preprint arXiv:1904.12428},
  year={2019}
}

If you have any problem, please feel free to contact us.

Related Work

Our work benefits from code of MUNIT, DRIT, StarGAN, etc.