Transferability for domain generalization

This repo is for evaluating and improving transferability in domain generalization (NeurIPS 2021), based on our paper Quantifying and Improving Transferability in Domain Generalization. The code is adapted from the DomainBed suite.

• python version: 3.6
• pytorch version: 1.7.1
• cuda version: 10.2

We aim to achieve two goals:

• measure the transferability between domains
• implement the Transfer algorithm

Currently we support four datasets:

• RotatedMNIST
• PACS
• OfficeHome
• WILDS-FMoW

To get started, first obtain a datasplit of a dataset. For example, if the dataset is RotatedMNIST, we run:

python save_datasets.py --dataset=RotatedMNIST

The next step is to run the training algorithm. For example, if we want to train ERM:

python -m train --algorithm=ERM --dataset=RotatedMNIST

The repo also supports the training of Transfer algorithm. For instance, if we want to train Transfer on RotatedMNIST with 30 steps per inner loop with projection radius 10.0:

python -m train --algorithm=Transfer --dataset=RotatedMNIST \
--output_dir="results" \
--steps=8000 \
--lr=0.01 \
--lr_d=0.01 \
--d_steps_per_g=30 \
--train_delta=10.0

Finally we could run evaluation after the training process. For example, if we want to evaluate ERM with delta=2.0:

python transfer_measure.py --algorithm=ERM --delta=2.0 --adv_epoch=10 --seed=0

Similarly, if we run:

python -m transfer_measure \
--d_steps_per_g=30 \
--train_delta=10.0 \
--algorithm=Transfer \
--dataset=RotatedMNIST \
--delta=2.0 \
--adv_epoch=10 \
--seed=0

We could evaluate the Transfer algorithm.

License

This source code is released under the MIT license, included here.

Citation

Comments are welcome! Please use the following bib if you use our code in your research:

@article{zhang2021quantifying,
      title={Quantifying and Improving Transferability in Domain Generalization}, 
      author={Guojun Zhang and Han Zhao and Yaoliang Yu and Pascal Poupart},
      year={2021},
      journal={Advances in neural information processing systems},
}