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} }