Relative attributing propagation (RAP) decomposes the output predictions of DNNs with a new perspective of separating the relevant (positive) and irrelevant (negative) attributions according to the relative influence between the layers. Detail description of this method is provided in our paper https://arxiv.org/pdf/1904.00605.pdf.
This paper has been accepted in AAAI 2020.
This code provides a implementation of RAP and LRP for Imagenet classification. For implementing other explaining methods in the paper, we followed the tutorial of http://heatmapping.org and https://github.com/albermax/innvestigate.
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
When using this code, please cite our paper.
@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).