pytorch >= 1.2.0
python >= 3.6
matplotlib >= 1.3.1

python main.py --method RAP --arc vgg
python main.py --method RAP --arc resnet

@misc{nam2019relative,
title={Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks},
author={Woo-Jeoung Nam and Shir Gur and Jaesik Choi and Lior Wolf and Seong-Whan Lee},
year={2019},
eprint={1904.00605},
archivePrefix={arXiv},
primaryClass={cs.CV}
}

This work was supported by Institute for Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT)
(No.2017-0-01779, A machine learning and statistical inference framework for explainable artificial intelligence & No.2019-0-01371,
Development of brain-inspired AI with human-like intelligence) and the European Research Council (ERC) under the European Unions Horizon 2020 research
and innovation  programme (grant ERC CoG 725974).