selfie2anime-danbooru | selfie-horse2zebra-dog | horse-cat2dog-anime | beetle-tiger2lion-sealion

• selfie2anime-danbooru   IrwGAN | [Baseline] | [CycleGAN] | [MUNIT] | [GcGAN] | [NICE-GAN]
• selfie-horse2zebra-dog   IrwGAN | [Baseline] | [CycleGAN] | [MUNIT] | [GcGAN] | [NICE-GAN]
• horse-cat2dog-anime     IrwGAN | [Baseline] | [CycleGAN] | [MUNIT] | [GcGAN] | [NICE-GAN]
• beetle-tiger2lion-sealion IrwGAN | [Baseline] | [CycleGAN] | [MUNIT] | [GcGAN] | [NICE-GAN]

• Training:

python main.py --dataroot=datasets/selfie2anime-danbooru 

• Resume:

python main.py --dataroot=datasets/selfie2anime-danbooru --phase=resume

• Test:

python main.py --dataroot=datasets/selfie2anime-danbooru --phase=test

• Beta Mode --beta_mode=A if domain A is unaligned, --beta_mode=B if domain B is unaligned, --beta_mode=AB if two domains are unaligned
• Effective Sample Size lambda_nos_A and lambda_nos_B are used to control how many samples are selected. The higher the weight, more samples are selected. We use 1.0 across all experiments.

If you use this code for your research, please cite our paper:

@inproceedings{xie2021unaligned,
  title={Unaligned Image-to-Image Translation by Learning to Reweight},
  author={Xie, Shaoan and Gong, Mingming and Xu, Yanwu and Zhang, Kun},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={14174--14184},
  year={2021}
}