Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Samek, Wojciech | - |
dc.contributor.author | Montavon, Gregoire | - |
dc.contributor.author | Lapuschkin, Sebastian | - |
dc.contributor.author | Anders, Christopher J. | - |
dc.contributor.author | Mueller, Klaus-Robert | - |
dc.date.accessioned | 2021-11-23T17:40:44Z | - |
dc.date.available | 2021-11-23T17:40:44Z | - |
dc.date.created | 2021-08-30 | - |
dc.date.issued | 2021-03 | - |
dc.identifier.issn | 0018-9219 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/128500 | - |
dc.description.abstract | With the broader and highly successful usage of machine learning (ML) in industry and the sciences, there has been a growing demand for explainable artificial intelligence (XAI). Interpretability and explanation methods for gaining a better understanding of the problem-solving abilities and strategies of nonlinear ML, in particular, deep neural networks, are, therefore, receiving increased attention. In this work, we aim to: 1) provide a timely overview of this active emerging field, with a focus on "post hoc" explanations, and explain its theoretical foundations; 2) put interpretability algorithms to a test both from a theory and comparative evaluation perspective using extensive simulations; 3) outline best practice aspects, i.e., how to best include interpretation methods into the standard usage of ML; and 4) demonstrate successful usage of XAI in a representative selection of application scenarios. Finally, we discuss challenges and possible future directions of this exciting foundational field of ML. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | BLACK-BOX | - |
dc.subject | MODELS | - |
dc.subject | CLASSIFICATION | - |
dc.subject | EXPLANATION | - |
dc.subject | PREDICTION | - |
dc.subject | DECISIONS | - |
dc.subject | IMAGES | - |
dc.title | Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Mueller, Klaus-Robert | - |
dc.identifier.doi | 10.1109/JPROC.2021.3060483 | - |
dc.identifier.scopusid | 2-s2.0-85101763532 | - |
dc.identifier.wosid | 000626523700003 | - |
dc.identifier.bibliographicCitation | PROCEEDINGS OF THE IEEE, v.109, no.3, pp.247 - 278 | - |
dc.relation.isPartOf | PROCEEDINGS OF THE IEEE | - |
dc.citation.title | PROCEEDINGS OF THE IEEE | - |
dc.citation.volume | 109 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 247 | - |
dc.citation.endPage | 278 | - |
dc.type.rims | ART | - |
dc.type.docType | Review | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | BLACK-BOX | - |
dc.subject.keywordPlus | MODELS | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | EXPLANATION | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordPlus | DECISIONS | - |
dc.subject.keywordPlus | IMAGES | - |
dc.subject.keywordAuthor | Black-box models | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | explainable artificial intelligence (XAI) | - |
dc.subject.keywordAuthor | Interpretability | - |
dc.subject.keywordAuthor | model transparency | - |
dc.subject.keywordAuthor | neural networks | - |
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