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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3872
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dc.contributor.authorStadelmann, Thilo-
dc.contributor.authorAmirian, Mohammadreza-
dc.contributor.authorArabaci, Ismail-
dc.contributor.authorArnold, Marek-
dc.contributor.authorDuivesteijn, Gilbert François-
dc.contributor.authorElezi, Ismail-
dc.contributor.authorGeiger, Melanie-
dc.contributor.authorLörwald, Stefan-
dc.contributor.authorMeier, Benjamin Bruno-
dc.contributor.authorRombach, Katharina-
dc.contributor.authorTuggener, Lukas-
dc.date.accessioned2018-07-16T08:46:33Z-
dc.date.available2018-07-16T08:46:33Z-
dc.date.issued2018-
dc.identifier.isbn978-3-319-99977-7de_CH
dc.identifier.isbn978-3-319-99978-4de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/8131-
dc.descriptionInvited paperde_CH
dc.description.abstractDeep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks. While this interest is fueled by beautiful success stories, practical work in deep learning on novel tasks without existing baselines remains challenging. This paper explores the specific challenges arising in the realm of real world tasks, based on case studies from research & development in conjunction with industry, and extracts lessons learned from them. It thus fills a gap between the publication of latest algorithmic and methodical developments, and the usually omitted nitty-gritty of how to make them work. Specifically, we give insight into deep learning projects on face matching, print media monitoring, industrial quality control, music scanning, strategy game playing, and automated machine learning, thereby providing best practices for deep learning in practice.de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofseriesLecture Notes in Computer Sciencede_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectData availabilityde_CH
dc.subjectDeploymentde_CH
dc.subjectLoss & reward shapingde_CH
dc.subjectReal world tasksde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleDeep learning in the wildde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.1007/978-3-319-99978-4_2de_CH
dc.identifier.doi10.21256/zhaw-3872-
zhaw.conference.details8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR), Siena, Italy, 19-21 September 2018de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end38de_CH
zhaw.pages.start17de_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.series.number11081de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsArtificial Neural Networks in Pattern Recognitionde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedInformation Engineeringde_CH
zhaw.webfeedNatural Language Processingde_CH
zhaw.webfeedMachine Perception and Cognitionde_CH
zhaw.funding.zhawLibra: A One-Tool Solution for MLD4 Compliancede_CH
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Stadelmann, T., Amirian, M., Arabaci, I., Arnold, M., Duivesteijn, G. F., Elezi, I., Geiger, M., Lörwald, S., Meier, B. B., Rombach, K., & Tuggener, L. (2018). Deep learning in the wild [Conference paper]. Artificial Neural Networks in Pattern Recognition, 17–38. https://doi.org/10.1007/978-3-319-99978-4_2
Stadelmann, T. et al. (2018) ‘Deep learning in the wild’, in Artificial Neural Networks in Pattern Recognition. Springer, pp. 17–38. Available at: https://doi.org/10.1007/978-3-319-99978-4_2.
T. Stadelmann et al., “Deep learning in the wild,” in Artificial Neural Networks in Pattern Recognition, 2018, pp. 17–38. doi: 10.1007/978-3-319-99978-4_2.
STADELMANN, Thilo, Mohammadreza AMIRIAN, Ismail ARABACI, Marek ARNOLD, Gilbert François DUIVESTEIJN, Ismail ELEZI, Melanie GEIGER, Stefan LÖRWALD, Benjamin Bruno MEIER, Katharina ROMBACH und Lukas TUGGENER, 2018. Deep learning in the wild. In: Artificial Neural Networks in Pattern Recognition. Conference paper. Springer. 2018. S. 17–38. ISBN 978-3-319-99977-7
Stadelmann, Thilo, Mohammadreza Amirian, Ismail Arabaci, Marek Arnold, Gilbert François Duivesteijn, Ismail Elezi, Melanie Geiger, et al. 2018. “Deep Learning in the Wild.” Conference paper. In Artificial Neural Networks in Pattern Recognition, 17–38. Springer. https://doi.org/10.1007/978-3-319-99978-4_2.
Stadelmann, Thilo, et al. “Deep Learning in the Wild.” Artificial Neural Networks in Pattern Recognition, Springer, 2018, pp. 17–38, https://doi.org/10.1007/978-3-319-99978-4_2.


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