MT-GAN-PyTorch - PyTorch Implementation of Learning to Transfer: Unsupervised Domain Translation via Meta-Learning

Overview

MT-GAN-PyTorch

PyTorch Implementation of AAAI-2020 Paper "Learning to Transfer: Unsupervised Domain Translation via Meta-Learning"

Dependency:

Python 3.6

PyTorch 0.4.0

Usage:

Unsupervised Domain Translation via Meta-Learning

  1. Downloading labels2photos, horses2zebras, summer2winter, apple2orange, monet2photo, cezanne2photo, ukiyoe2photo, vangogh2photo, photos2maps and labels2facades datasets following CycleGAN.

  2. Organize these translation datasets as:

    meta_datarooot
    ├── vangogh2photo
    |   ├── trainA
    |   ├── trainB
    |   
    ├── ukiyoe2photo
        ├── trainA
        ├── trainB
    ...
    
  3. Train MT-GAN on 10-shot tranlation:

    $ python train.py --name mtgan_results --model mt_gan --meta_dataroot meta_datarooot --k_spt 10 --k_qry 10 --finetune_step 1000

  4. Test MT-GAN on 10-shot translation:

    $ python test.py --name mtgan_results --model mt_gan --meta_dataroot meta_datarooot --k_spt 10 --k_qry 10 --finetune_step 1000

  5. Train MT-GAN on 5-shot translation:

    $ python train.py --name mtgan_results --model mt_gan --meta_dataroot meta_datarooot --k_spt 5 --k_qry 5 --finetune_step 1000

  6. Test MT-GAN on 5-shot translation:

    $ python test.py --name mtgan_results --model mt_gan --meta_dataroot meta_datarooot --k_spt 5 --k_qry 5 --finetune_step 1000

Owner
Ph.D. Candidate of University of Science and Technology of China
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