dmit's Introduction

DMIT

Pytorch implementation of our paper: "Multi-mapping Image-to-Image Translation via Learning Disentanglement".

Dependencies

you can install all the dependencies by

pip install -r requirements.txt

Getting Started

Datasets

Download and unzip preprocessed datasets by

Season Transfer

 bash ./scripts/download_datasets.sh summer2winter_yosemite

Semantic Image Synthesis

 bash ./scripts/download_datasets.sh birds

Or you can manually download them from CycleGAN and AttnGAN.

Training

Season Transfer

 bash ./scripts/train_season_transfer.sh

Semantic Image Synthesis

 bash ./scripts/train_semantic_image_synthesis.sh

To view training results and loss plots, run python -m visdom.server and click the URL http://localhost:8097. More intermediate results can be found in environment exp_name.

Testing

Run

 bash ./scripts/test_season_transfer.sh
 bash ./scripts/test_semantic_image_synthesis.sh

The testing results will be saved in checkpoints/{exp_name}/results directory.

Pretrained Models

Pretrained models can be downloaded from Google Drive or Baidu Wangpan with code 59tm.

Custom Experiment

You can implement your Dataset and SubModel to start a new experiment.

Results

Season Transfer:

Semantic Image Synthesis:

bibtex

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

@inproceedings{yu2019multi,
  title={Multi-mapping Image-to-Image Translation via Learning Disentanglement},
  author={Yu, Xiaoming and Chen, Yuanqi and Liu, Shan and Li, Thomas and Li, Ge},
  booktitle={Advances in Neural Information Processing Systems},
  year={2019}
}