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This repository holds the Pytorch implementation of Distributionally Robust Explanations (DRE) in Are Data-driven Explanations Robust against Out-of-distribution Data? by Tang Li, Fengchun Qiao, Mengmeng Ma, and Xi Peng. If you find our code useful in your research, please consider citing:

@inproceedings{li2023dre,
 title={Are Data-driven Explanations Robust against Out-of-distribution Data?},
 author={Li, Tang and Qiao, Fengchun and Ma, Mengmeng and Peng, Xi},
 booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
 year={2023}
}

We study the out-of-distribution (OOD) robustness of data-driven explanations. Our evaluations prove that data-driven explanations are susceptible to distributional shifts. However, acquiring the ground truth explanations for all samples or obtaining the one-to-one mapping between samples from different distributions are prohibitively expensive or even impossible in practice. To this end, we propose Distributionally Robust Explanation (DRE) that, inspired by self-supervised learning, leveraging the mixed explanation to provide supervisory signals for the learning of explanations.

DRE models:

• Terra Incognita: [tst_env=0] [tst_env=1] [tst_env=2] [tst_env=3]
• VLCS: [tst_env=0] [tst_env=1] [tst_env=2] [tst_env=3]

This repository reproduces our results on Terra Incognita and VLCS, which is build upon Python3, Pytorch v1.12.1, and CUDA v10.2 on Ubuntu 18.04. Please install all required packages by running:

pip install -r requirements.txt

To download the datasets, please run:

python download.py --data_dir=./

Please note that some URLs may not work due to various factors. You can copy the URLs and download them manually.

The results for explanation quality and prediction accuracy:

To reproduce the results of our DRE method, please run:

python -m dre.train \
      --dataset terra_incognita \
      --model DRE  

To reproduce the results of baseline ERM method, please run:

python -m dre.train \
      --dataset terra_incognita \
      --model ERM  

For other baselines, such as IRM, GroupDRO, and Mixup, please run (you can specify the baseline method by changing the algorithm):

python3 -m domainbed.scripts.train \
       --data_dir=./data/ \
       --algorithm IRM \
       --dataset terra_incognita \
       --test_env 0

The explanations using Grad-CAM:

To reproduce the explanation comparison between DRE and baseline methods, please run the notebooks in "./dre/explanations/visualizations/". For example, the Grad-CAM comparison between DRE and ERM:

./dre/explanations/visualizations/grad_cam_erm.ipynb

To reproduce the explanation fidelity results, please run:

python -m dre.explanations.fidelity.evaluate_auc \
      --ckpt-path ../../ckpts/best_model.pth \
      --root ../../../data/terra_incognita/location_38

• Training code
• Evaluation code
• Terra Incognita
• VLCS
• Urban Land

Part of our code is borrowed from the following repositories.

• DomainBed
• Captum
• SHAP
• RISE

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