This repository holds the Pytorch implementation of Learning to Learn Single Domain Generalization by Fengchun Qiao, Long Zhao and Xi Peng.

We are concerned with a worst-case scenario in model generalization, in the sense that a model aims to perform well on many unseen domains while there is only one single domain available for training. We propose a new method named adversarial domain augmentation to solve this Out-of-Distribution (OOD) generalization problem. The key idea is to leverage adversarial training to create “fictitious” yet “challenging” populations, from which a model can learn to generalize with theoretical guarantees.

This package has the following requirements:

• Python 3.6
• Pytorch 1.1.0
• MetaNN 0.1.5
• Scipy 1.2.1

Download and process MNIST:

cd utils
python download_and_process_mnist.py

Run the following command:

python main_Digits.py

The pretrained models can be downloaded from Google Drive. Put checkpoint in the project root directory.

To evaluate on Digits, run:

mkdir ./data/svhn
wget http://ufldl.stanford.edu/housenumbers/test_32x32.mat -O "./data/svhn/test_32x32.mat"  # download the test set of SVHN
python main_Digits.py --mode test --resume checkpoint/pretrained/ckpt_mnist.pth.tar

If you find our code useful in your research, please consider citing:

@inproceedings{qiaoCVPR20learning,
  title={Learning to learn single domain generalization},
  author={Qiao, Fengchun and Zhao, Long and Peng, Xi},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={12556--12565},
  year={2020}
}

Part of our code is borrowed from the following repositories.

• generalize-unseen-domains
• WideResNet-pytorch
• WAE-pytorch

We thank to the authors for releasing their codes. Please also consider citing their works.