TY - GEN
T1 - Visual Explanations via Iterated Integrated Attributions
AU - Barkan, Oren
AU - Elisha, Yehonatan
AU - Asher, Yuval
AU - Eshel, Amit
AU - Koenigstein, Noam
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - We introduce Iterated Integrated Attributions (IIA) - a generic method for explaining the predictions of vision models. IIA employs iterative integration across the input image, the internal representations generated by the model, and their gradients, yielding precise and focused explanation maps. We demonstrate the effectiveness of IIA through comprehensive evaluations across various tasks, datasets, and network architectures. Our results showcase that IIA produces accurate explanation maps, outperforming other state-of-the-art explanation techniques.
AB - We introduce Iterated Integrated Attributions (IIA) - a generic method for explaining the predictions of vision models. IIA employs iterative integration across the input image, the internal representations generated by the model, and their gradients, yielding precise and focused explanation maps. We demonstrate the effectiveness of IIA through comprehensive evaluations across various tasks, datasets, and network architectures. Our results showcase that IIA produces accurate explanation maps, outperforming other state-of-the-art explanation techniques.
UR - http://www.scopus.com/inward/record.url?scp=85175598274&partnerID=8YFLogxK
U2 - 10.1109/ICCV51070.2023.00198
DO - 10.1109/ICCV51070.2023.00198
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AN - SCOPUS:85175598274
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 2073
EP - 2084
BT - Proceedings - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE/CVF International Conference on Computer Vision, ICCV 2023
Y2 - 2 October 2023 through 6 October 2023
ER -