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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".

Demo

Requirements

Docker image:

nvcr.io/milut/medical/krs-pytorch-demo:1.0.0

command

sudo chmod 666 /dev/video0

docker run command:

docker run -it --gpus all --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 -e DISPLAY=unix${DISPLAY} -v /tmp/.X11-unix:/tmp/.X11-unix -u `id -u` -v ${HOME}:${HOME} -w ${HOME} --device /dev/video1:/dev/video1:mwr --device /dev/video0:/dev/video0:mwr nvcr.io/milut/medical/krs-pytorch-demo:1.0.0 /bin/bash

command:

python gui.py

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},
  }