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dc.contributor.authorSagemüller, Justus
dc.contributor.authorVerdier, Olivier Philippe Paul
dc.date.accessioned2023-11-21T10:18:36Z
dc.date.available2023-11-21T10:18:36Z
dc.date.created2023-11-17T15:49:42Z
dc.date.issued2023
dc.identifier.citationCommunications in Computer and Information Science (CCIS). 2023, 1901 349-372.en_US
dc.identifier.issn1865-0929
dc.identifier.urihttps://hdl.handle.net/11250/3103808
dc.descriptionThis version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at https://doi.org/10.1007/978-3-031-44064-9_19en_US
dc.description.abstractVarious types of saliency methods have been proposed for explaining black-box classification. In image applications, this means highlighting the part of the image that is most relevant for the current decision. Unfortunately, the different methods may disagree and it can be hard to quantify how representative and faithful the explanation really is. We observe however that several of these methods can be seen as edge cases of a single, more general procedure based on finding a particular path through the classifier’s domain. This offers additional geometric interpretation to the existing methods. We demonstrate furthermore that ablation paths can be directly used as a technique of its own right. This is able to compete with literature methods on existing benchmarks, while giving more fine-grained information and better opportunities for validation of the explanations’ faithfulness.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.titleAblation Path Saliencyen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.source.pagenumber349-372en_US
dc.source.volume1901en_US
dc.source.journalCommunications in Computer and Information Science (CCIS)en_US
dc.identifier.doi10.1007/978-3-031-44064-9_19
dc.identifier.cristin2198271
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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