meta-Domain Specific-Domain Invariant (mDSDI)

Source code implementation for the paper:

Manh-Ha Bui, Toan Tran, Anh Tuan Tran, Dinh Phung. "Exploiting Domain-Specific Features to Enhance Domain Generalization". Advances in Neural Information Processing Systems (NeurIPS | 2021).

Guideline

To prepare:

Install prerequisite packages:

python -m pip install -r requirements.txt

Download and unzip the datasets:

bash setup.sh

To run experiments:

Run with five different seeds:

for i in {1..3}; do
     taskset -c <cpu_index> python main.py --config <config_path> --exp_idx $i --gpu_idx <gpu_index>
done

where the parameters are the following:

• <cpu_index>: CPU index. E.g., <cpu_index> = "1"
• <config_path>: path stored configuration hyper-parameters. E.g., <config_path> = "algorithms/mDSDI/configs/PACS_photo.json"
• <gpu_index>: GPU index. E.g., <gpu_index> = "0"

Note: Select different settings by editing in /configs/..json, logging results are stored in /results/logs/

To visualize objective functions:

tensorboard --logdir <logdir>

where <logdir>: absolute path stored TensorBoard results. E.g., <logdir> = "/home/ubuntu/mDSDI/algorithms/mDSDI/results/tensorboards/PACS_photo_1"

To plot feature representations:

python utils/tSNE_plot.py --plotdir <plotdir>

where <plotdir>: path stored results to plot. E.g., <plotdir> = "algorithms/mDSDI/results/plots/PACS_photo_1/"

Note: Results are stored in /results/plots/

To run on "DomainBed, Ishaan and David, 2021" library:

cd DomainBed/
python -m domainbed.scripts.train --data_dir=../data/ --algorithm MDSDI --dataset <dataset_name> --test_env <env_idx>

where the parameters are the following:

• <dataset_name>: name of 5 benchmark datasets, including: RotatedMNIST | VLCS | OfficeHome | PACS | DomainNet. E.g., <dataset_name> = PACS
• <test_env>: index of the target domain. E.g., <dataset_name> = 0

Note: Results are stored in DomainBed/results/train_output/out.txt