@inproceedings{93e1f9cf875f488ca5ccdeb6fa832c13,
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 = "https://doi.org/10.1145/3583780.3614836",
language = "الإنجليزيّة",
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",
}