Bi-level feature alignment for versatile image translation and manipulation (Under submission of TPAMI)

Related tags

Deep LearningRABIT
Overview

Bi-level feature alignment for versatile image translation and manipulation (Under submission of TPAMI)

Teaser

Preparation

Clone the Synchronized-BatchNorm-PyTorch repository.

cd models/networks/
git clone https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
cp -rf Synchronized-BatchNorm-PyTorch/sync_batchnorm .
cd ../../

VGG model for computing loss. Download from here, move it to models/.

For the preparation of datasets, please refer to CoCosNet.

Training

Then run the command

bash train_ade.sh

Citation

If you use this code for your research, please cite our papers.

@article{zhan2021rabit,
  title={Bi-level feature alignment for versatile image translation and manipulation},
  author={Zhan, Fangneng and Yu, Yingchen and Wu, Rongliang and Cui, Kaiwen and Xiao, Aoran and Lu, Shijian and Shao, Ling},
  journal={arXiv preprint arXiv:2107.03021},
  year={2021}
}

Acknowledgments

This code borrows heavily from CoCosNet. We also thank SPADE, Synchronized Normalization.

Owner
Fangneng Zhan
Computer Vision, Deep Learning.
Fangneng Zhan
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