Deep Integrated Explanations

Oren Barkan; Yehonathan Elisha; Jonathan Weill; Yuval Asher; Amit Eshel; Noam Koenigstein

doi:10.1145/3583780.3614836

Deep Integrated Explanations

Oren Barkan, Yehonathan Elisha, Jonathan Weill, Yuval Asher, Amit Eshel, Noam Koenigstein

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

This paper presents Deep Integrated Explanations (DIX) - a universal method for explaining vision models. DIX generates explanation maps by integrating information from the intermediate representations of the model, coupled with their corresponding gradients. Through an extensive array of both objective and subjective evaluations spanning diverse tasks, datasets, and model configurations, we showcase the efficacy of DIX in generating faithful and accurate explanation maps, while surpassing current state-of-the-art methods. Our code is available at: https://github.com/dix-cikm23/dix.

Original language	English
Title of host publication	CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
Pages	57-67
Number of pages	11
ISBN (Electronic)	9798400701245
DOIs	https://doi.org/10.1145/3583780.3614836
State	Published - 21 Oct 2023
Externally published	Yes
Event	32nd ACM International Conference on Information and Knowledge Management, CIKM 2023 - Birmingham, United Kingdom Duration: 21 Oct 2023 → 25 Oct 2023

Publication series

Name	International Conference on Information and Knowledge Management, Proceedings

Conference

Conference	32nd ACM International Conference on Information and Knowledge Management, CIKM 2023
Country/Territory	United Kingdom
City	Birmingham
Period	21/10/23 → 25/10/23

Keywords

Computer Vision
Deep Learning
Explainable AI

Access to Document

10.1145/3583780.3614836

Cite this

@inproceedings{dec64f8411184adc9e213f33ccca4299,

title = "Deep Integrated Explanations",

abstract = "This paper presents Deep Integrated Explanations (DIX) - a universal method for explaining vision models. DIX generates explanation maps by integrating information from the intermediate representations of the model, coupled with their corresponding gradients. Through an extensive array of both objective and subjective evaluations spanning diverse tasks, datasets, and model configurations, we showcase the efficacy of DIX in generating faithful and accurate explanation maps, while surpassing current state-of-the-art methods. Our code is available at: https://github.com/dix-cikm23/dix.",

keywords = "Computer Vision, Deep Learning, Explainable AI",

author = "Oren Barkan and Yehonathan Elisha and Jonathan Weill and Yuval Asher and Amit Eshel and Noam Koenigstein",

note = "Publisher Copyright: {\textcopyright} 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.; 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023 ; Conference date: 21-10-2023 Through 25-10-2023",

year = "2023",

month = oct,

day = "21",

doi = "10.1145/3583780.3614836",

language = "אנגלית",

series = "International Conference on Information and Knowledge Management, Proceedings",

pages = "57--67",

booktitle = "CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management",

}

Barkan, O, Elisha, Y, Weill, J, Asher, Y, Eshel, A & Koenigstein, N 2023, Deep Integrated Explanations. in CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. International Conference on Information and Knowledge Management, Proceedings, pp. 57-67, 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, 21/10/23. https://doi.org/10.1145/3583780.3614836

Deep Integrated Explanations. / Barkan, Oren; Elisha, Yehonathan; Weill, Jonathan et al.
CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 2023. p. 57-67 (International Conference on Information and Knowledge Management, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Deep Integrated Explanations

AU - Barkan, Oren

AU - Elisha, Yehonathan

AU - Weill, Jonathan

AU - Asher, Yuval

AU - Eshel, Amit

AU - Koenigstein, Noam

PY - 2023/10/21

Y1 - 2023/10/21

N2 - This paper presents Deep Integrated Explanations (DIX) - a universal method for explaining vision models. DIX generates explanation maps by integrating information from the intermediate representations of the model, coupled with their corresponding gradients. Through an extensive array of both objective and subjective evaluations spanning diverse tasks, datasets, and model configurations, we showcase the efficacy of DIX in generating faithful and accurate explanation maps, while surpassing current state-of-the-art methods. Our code is available at: https://github.com/dix-cikm23/dix.

AB - This paper presents Deep Integrated Explanations (DIX) - a universal method for explaining vision models. DIX generates explanation maps by integrating information from the intermediate representations of the model, coupled with their corresponding gradients. Through an extensive array of both objective and subjective evaluations spanning diverse tasks, datasets, and model configurations, we showcase the efficacy of DIX in generating faithful and accurate explanation maps, while surpassing current state-of-the-art methods. Our code is available at: https://github.com/dix-cikm23/dix.

KW - Computer Vision

KW - Deep Learning

KW - Explainable AI

UR - http://www.scopus.com/inward/record.url?scp=85175686880&partnerID=8YFLogxK

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T3 - International Conference on Information and Knowledge Management, Proceedings

SP - 57

EP - 67

BT - CIKM 2023 - Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

T2 - 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023

Y2 - 21 October 2023 through 25 October 2023

ER -

Deep Integrated Explanations

Abstract

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Keywords

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