Evaluating the faithfulness of saliency maps in explaining deep learning models using realistic perturbations.
José Pereira Amorim|Pedro H. Abreu|João A. M. Santos|Marc Cortes|Victor Vila
| Anthology ID: | DBLP:journals/ipm/AmorimASCV23 |
|---|---|
| Year: | 2023 |
| Venue: | Information Processing and Management |
| Pages: | 103225 |
| URL: | https://doi.org/10.1016/J.IPM.2022.103225 |
| DOI: | https://doi.org/10.1016/J.IPM.2022.103225 |
| DBLP: | journals/ipm/AmorimASCV23 |
| BibTeX: |
@article{amorim-2023-evaluating,
author = {Jos\'{e} Pereira Amorim and
Pedro H. Abreu and
Jo\~{a}o A. M. Santos and
Marc Cortes and
Victor Vila},
title = {{Evaluating the faithfulness of saliency maps in explaining deep learning models using realistic perturbations}},
journal = {Information Processing and Management},
volume = {60},
number = {2},
pages = {103225},
year = {2023},
url = {https://doi.org/10.1016/J.IPM.2022.103225},
doi = {https://doi.org/10.1016/J.IPM.2022.103225}
}