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Towards Explainable Deep Domain Adaptation
Jagiellonian University, Kraków, Poland.ORCID iD: 0000-0002-6350-8405
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0002-7796-5201
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0003-3272-4145
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0002-1759-8593
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2024 (English)In: Artificial Intelligence. ECAI 2023 International Workshops: XAI^3, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30 – October 4, 2023, Proceedings, Part I / [ed] Sławomir Nowaczyk et al., Cham: Springer, 2024, Vol. 1947, p. 101-113Conference paper, Published paper (Refereed)
Abstract [en]

In many practical applications data used for training a machine learning model and the deployment data does not always preserve the same distribution. Transfer learning and, in particular, domain adaptation allows to overcome this issue, by adapting the source model to a new target data distribution and therefore generalizing the knowledge from source to target domain. In this work, we present a method that makes the adaptation process more transparent by providing two complementary explanation mechanisms. The first mechanism explains how the source and target distributions are aligned in the latent space of the domain adaptation model. The second mechanism provides descriptive explanations on how the decision boundary changes in the adapted model with respect to the source model. Along with a description of a method, we also provide initial results obtained on publicly available, real-life dataset. © The Author(s) 2024.

Place, publisher, year, edition, pages
Cham: Springer, 2024. Vol. 1947, p. 101-113
Series
Communications in Computer and Information Science (CCIS), ISSN 1865-0929, E-ISSN 1865-0937 ; 1947
Keywords [en]
Explainable AI (XAI), Domain adaptation, artificial intelligence
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:hh:diva-52508DOI: 10.1007/978-3-031-50396-2_6Scopus ID: 2-s2.0-85184123743ISBN: 978-3-031-50395-5 (print)ISBN: 978-3-031-50396-2 (electronic)OAI: oai:DiVA.org:hh-52508DiVA, id: diva2:1832994
Conference
European Conference on Artificial Intelligence (ECAI 2023), Kraków, Poland, September 30 - October 4, 2023
Funder
Swedish Research Council, CHIST-ERA19-XAI-012
Note

Funding: The paper is funded from the XPM project funded by the National Science Centre, Poland under the CHIST-ERA programme (NCN UMO2020/02/Y/ST6/00070) and the Swedish Research Council under grant CHIST-ERA19-XAI-012 and by a grant from the Priority Research Area (DigiWorld) under the Strategic Programme Excellence Initiative at Jagiellonian University.

Available from: 2024-01-31 Created: 2024-01-31 Last updated: 2024-03-20Bibliographically approved

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Nowaczyk, SławomirPashami, SepidehTaghiyarrenani, Zahra

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