Explainable Models with Consistent Interpretations
| dc.contributor.author | Pillai, Vipin | |
| dc.contributor.author | Pirsiavash, Hamed | |
| dc.date.accessioned | 2021-02-19T16:44:14Z | |
| dc.date.available | 2021-02-19T16:44:14Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Given the widespread deployment of black box deep neural networks in computer vision applications, the interpretability aspect of these black box systems has recently gained traction. Various methods have been proposed to explain the results of such deep neural networks. However, some recent works have shown that such explanation methods are biased and do not produce consistent interpretations. Hence, rather than introducing a novel explanation method, we learn models that are encouraged to be interpretable given an explanation method. We use Grad-CAM as the explanation algorithm and encourage the network to learn consistent interpretations along with maximizing the log-likelihood of the correct class. We show that our method outperforms the baseline on the pointing game evaluation on ImageNet and MS-COCO datasets respectively. We also introduce new evaluation metrics that penalize the saliency map if it lies outside the ground truth bounding box or segmentation mask, and show that our method outperforms the baseline on these metrics as well. Moreover, our model trained with interpretation consistency generalizes to other explanation algorithms on all the evaluation metrics. | en_US |
| dc.description.sponsorship | This material is based upon work partially supported by the United States Air Force under Contract No. FA8750-19-C-0098, funding from NSF grant number 1845216, SAP SE, and Northrop Grumman. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the United States Air Force, DARPA, or other funding agencies. | en_US |
| dc.description.uri | https://www.aaai.org/AAAI21Papers/AAAI-8236.PillaiV.pdf | en_US |
| dc.format.extent | 9 pages | en_US |
| dc.genre | journal articles | en_US |
| dc.genre | preprints | |
| dc.identifier | doi:10.13016/m2orpw-45bj | |
| dc.identifier.citation | Pillai, Vipin; Pirsiavash, Hamed; Explainable Models with Consistent Interpretations (2021); https://www.aaai.org/AAAI21Papers/AAAI-8236.PillaiV.pdf | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/21053 | |
| dc.identifier.uri | https://doi.org/10.1609/aaai.v35i3.16344 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | AAAI | |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.subject | deep neural networks | en_US |
| dc.subject | evaluation metrics | en_US |
| dc.subject | algorithms | en_US |
| dc.title | Explainable Models with Consistent Interpretations | en_US |
| dc.type | Text | en_US |