1. Introduction
2. Guideline
   • To prepare
   • To run experiments
   • To visualize objective functions
   • To plot feature representations
   • To run on DomainBed, Ishaan and David, 2021 library

This repository contains the implementation of meta-Domain Specific-Domain Invariant (mDSDI), a theoretical sound framework based on the Information-Bottleneck principle to investigate foundational questions in Domain Generalization (DG). Following the theoretical analysis of the limitation when only learning domain-invariant and the essential of domain-specific representation, mDSDI extends beyond the invariance view by adversarial training to further capture the usefulness of domain-specific information by meta-training. Its results show competitive performance with state-of-the-art techniques in DG and confirm the hypothesis that domain-specific is essential in ablation studies.

Details of the model and experimental results can be found in our following NeurIPS 2021 paper:

@inproceedings{NEURIPS2021_b0f2ad44,
 author = {Bui, Manh-Ha and Tran, Toan and Tran, Anh and Phung, Dinh},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan},
 pages = {21189--21201},
 publisher = {Curran Associates, Inc.},
 title = {Exploiting Domain-Specific Features to Enhance Domain Generalization},
 url = {https://proceedings.neurips.cc/paper/2021/file/b0f2ad44d26e1a6f244201fe0fd864d1-Paper.pdf},
 volume = {34},
 year = {2021}
}

Please CITE our paper if you find it useful in your research.

Install prerequisite packages:

python -m pip install -r requirements.txt

Download and unzip the datasets:

bash setup.sh

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/

tensorboard --logdir <logdir>

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

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/

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

This source code is released under the Apache-2.0 license, included here.