Skip to content

Pytorch Implementation of Domain Generalization Using a Mixture of Multiple Latent Domains

Notifications You must be signed in to change notification settings

mil-tokyo/dg_mmld

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

18 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Domain Generalization Using a Mixture of Multiple Latent Domains

model This is the pytorch implementation of the AAAI 2020 poster paper "Domain Generalization Using a Mixture of Multiple Latent Domains".

Requirements

  • A Python install version 3.6
  • A PyTorch and torchvision installation version 0.4.1 and 0.2.1, respectively. pytorch.org
  • The caffe model we used for AlexNet
  • PACS dataset (website, dateset)
  • Install python requirements
pip install -r requirements.txt

Training and Testing

You can train the model using the following command.

cd script
bash general.sh

If you want to train the model without domain generalization (Deep All), you can also use the following command.

cd script
bash deepall.sh

You can set the correct parameter.

  • --data-root: the dataset folder path
  • --save-root: the folder path for saving the results
  • --gpu: the gpu id to run experiments

Citation

If you use this code, please cite the following paper:

Toshihiko Matsuura and Tatsuya Harada. Domain Generalization Using a Mixture of Multiple Latent Domains. In AAAI, 2020.

@InProceedings{dg_mmld,
  title={Domain Generalization Using a Mixture of Multiple Latent Domains},
  author={Toshihiko Matsuura and Tatsuya Harada},
  booktitle={AAAI},
  year={2020},
  }

About

Pytorch Implementation of Domain Generalization Using a Mixture of Multiple Latent Domains

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published