{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T03:12:48Z","timestamp":1790219568759,"version":"4.1.0"},"reference-count":53,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T00:00:00Z","timestamp":1713139200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2024,6,30]]},"abstract":"<jats:p>\n            Recent advancement in deep-neural network performance led to the development of new state-of-the-art approaches in numerous areas. However, the black-box nature of neural networks often prohibits their use in areas where model explainability and model transparency are crucial. Over the years, researchers proposed many algorithms to aid neural network understanding and provide additional information to the human expert. One of the most popular methods being Layer-Wise Relevance Propagation (LRP). This method assigns local relevance based on the pixel-wise decomposition of nonlinear classifiers. With the rise of attribution method research, there has emerged a pressing need to assess and evaluate their performance. Numerous metrics have been proposed, each assessing an individual property of attribution methods such as faithfulness, robustness, or localization. Unfortunately, no single metric is deemed optimal for every case, and researchers often use several metrics to test the quality of the attribution maps. In this work, we address the shortcomings of the current LRP formulations and introduce a novel method for determining the relevance of input neurons through layer-wise relevance propagation. Furthermore, we apply this approach to the recently developed Vision Transformer architecture and evaluate its performance against existing methods on two image classification datasets, namely ImageNet and PascalVOC. Our results clearly demonstrate the advantage of our proposed method. Furthermore, we discuss the insufficiencies of current evaluation metrics for attribution-based explainability and propose a new evaluation metric that combines the notions of faithfulness, robustness, and contrastiveness. We utilize this new metric to evaluate the performance of various attribution-based methods. Our code is available at:\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"url\" xlink:href=\"https:\/\/github.com\/davor10105\/relative-absolute-magnitude-propagation\">https:\/\/github.com\/davor10105\/relative-absolute-magnitude-propagation<\/jats:ext-link>\n          <\/jats:p>","DOI":"10.1145\/3649458","type":"journal-article","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T12:35:13Z","timestamp":1708950913000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Advancing Attribution-Based Neural Network Explainability through Relative Absolute Magnitude Layer-Wise Relevance Propagation and Multi-Component Evaluation"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3309-6718","authenticated-orcid":false,"given":"Davor","family":"Vukadin","sequence":"first","affiliation":[{"name":"University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9270-5988","authenticated-orcid":false,"given":"Petar","family":"Afri\u0107","sequence":"additional","affiliation":[{"name":"University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4896-7689","authenticated-orcid":false,"given":"Marin","family":"\u0160ili\u0107","sequence":"additional","affiliation":[{"name":"University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5315-8387","authenticated-orcid":false,"given":"Goran","family":"Dela\u010d","sequence":"additional","affiliation":[{"name":"University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,4,15]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.385"},{"key":"e_1_3_2_3_2","first-page":"435","volume-title":"Proceedings of the International Conference on Machine Learning, ICML 2022 (Proceedings of Machine Learning Research, Vol. 162)","author":"Ali Ameen","year":"2022","unstructured":"Ameen Ali, Thomas Schnake, Oliver Eberle, Gr\u00e9goire Montavon, Klaus-Robert M\u00fcller, and Lior Wolf. 2022. XAI for transformers: Better explanations through conservative propagation. In Proceedings of the International Conference on Machine Learning, ICML 2022 (Proceedings of Machine Learning Research, Vol. 162). PMLR, Baltimore, Maryland, USA, 435\u2013451. Retrieved from https:\/\/proceedings.mlr.press\/v162\/ali22a.html"},{"key":"e_1_3_2_4_2","volume-title":"Advances in Neural Information Processing Systems","author":"Melis David Alvarez","year":"2018","unstructured":"David Alvarez Melis and Tommi Jaakkola. 2018. Towards robust interpretability with self-explaining neural networks. In Advances in Neural Information Processing Systems. S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.), Vol. 31. Curran Associates, Inc., Montr\u00e9al, Canada. Retrieved from https:\/\/proceedings.neurips.cc\/paper\/2018\/file\/3e9f0fc9b2f89e043bc6233994dfcf76-Paper.pdf"},{"key":"e_1_3_2_5_2","unstructured":"David Alvarez-Melis and Tommi S. Jaakkola. 2018. 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