mlsanityData to 14 Oct 2026

Explainable Supervised Domain Adaptation

arXiv 2205.09943 ↗First posted 20 May 2022cs.LG cs.AIcited by 1

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Abstract (arXiv)

Domain adaptation techniques have contributed to the success of deep learning. Leveraging knowledge from an auxiliary source domain for learning in labeled data-scarce target domain is fundamental to domain adaptation. While these techniques result in increasing accuracy, the adaptation process, particularly the knowledge leveraged from the source domain, remains unclear. This paper proposes an explainable by design supervised domain adaptation framework - XSDA-Net. We integrate a case-based reasoning mechanism into the XSDA-Net to explain the prediction of a test instance in terms of similar-looking regions in the source and target train images. We empirically demonstrate the utility of the proposed framework by curating the domain adaptation settings on datasets popularly known to exhibit part-based explainability.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 30 of its 39 references that are in the map. First posted on arXiv 20 May 2022.

Builds on

30 of 39 references.

References · 30

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18 more
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  1. 2023Trustworthy Representation Learning Across Domainscited by 1