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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3175
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dc.contributor.authorStadelmann, Thilo-
dc.contributor.authorTolkachev, Vasily-
dc.contributor.authorSick, Beate-
dc.contributor.authorStampfli, Jan-
dc.contributor.authorDürr, Oliver-
dc.date.accessioned2019-07-04T12:30:10Z-
dc.date.available2019-07-04T12:30:10Z-
dc.date.issued2019-06-14-
dc.identifier.isbn978-3-030-11821-1de_CH
dc.identifier.isbn978-3-030-11820-4de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/17425-
dc.description.abstractDeep learning (DL) methods have gained considerable attention since 2014. In this chapter we briefly review the state of the art in DL and then give several examples of applications from diverse areas of application. We will focus on convolutional neural networks (CNNs), which have since the seminal work of Krizhevsky et al. (2012) revolutionized image classification and even started surpassing human performance on some benchmark data sets (Ciresan et al., 2012a, He et al., 2015a). While deep neural networks have become popular primarily for image classification tasks, they can also be successfully applied to other areas and problems with some local structure in the data. We will first present a classical application of CNNs on image-like data, in particular, phenotype classification of cells based on their morphology, and then extend the task to clustering voices based on their spectrograms. Next, we will describe DL applications to semantic segmentation of newspaper pages into their corresponding articles based on clues in the pixels, and outlier detection in a predictive maintenance setting. We conclude by giving advice on how to work with DL having limited resources (e.g., training data).de_CH
dc.language.isoende_CH
dc.publisherSpringerde_CH
dc.relation.ispartofApplied data science : lessons learned for the data-driven businessde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectDeep learningde_CH
dc.subjectConvolutional neural networkde_CH
dc.subjectImage recognitionde_CH
dc.subjectClassificationde_CH
dc.subjectAnomaly detectionde_CH
dc.subjectSpeaker clusteringde_CH
dc.subjectSpeaker recognitionde_CH
dc.subjectCNNde_CH
dc.subjectBiomedical data analysisde_CH
dc.subjectPredictive maintenancede_CH
dc.subjectFully convolutional neural networkde_CH
dc.subjectDocument analysisde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleBeyond ImageNet : deep learning in industrial practicede_CH
dc.typeBuchbeitragde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
zhaw.publisher.placeChamde_CH
dc.identifier.doi10.1007/978-3-030-11821-1_12de_CH
dc.identifier.doi10.21256/zhaw-3175-
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end232de_CH
zhaw.pages.start205de_CH
zhaw.parentwork.editorBraschler, Martin-
zhaw.parentwork.editorStadelmann, Thilo-
zhaw.parentwork.editorStockinger, Kurt-
zhaw.publication.statussubmittedVersionde_CH
zhaw.publication.reviewEditorial reviewde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedDigital Health Labde_CH
zhaw.webfeedInformation Engineeringde_CH
zhaw.webfeedZHAW digitalde_CH
zhaw.webfeedMachine Perception and Cognitionde_CH
zhaw.funding.zhawDaCoMo - Data-Driven Condition Monitoringde_CH
zhaw.funding.zhawPANOPTESde_CH
zhaw.author.additionalNode_CH
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Stadelmann, T., Tolkachev, V., Sick, B., Stampfli, J., & Dürr, O. (2019). Beyond ImageNet : deep learning in industrial practice. In M. Braschler, T. Stadelmann, & K. Stockinger (Eds.), Applied data science : lessons learned for the data-driven business (pp. 205–232). Springer. https://doi.org/10.1007/978-3-030-11821-1_12
Stadelmann, T. et al. (2019) ‘Beyond ImageNet : deep learning in industrial practice’, in M. Braschler, T. Stadelmann, and K. Stockinger (eds) Applied data science : lessons learned for the data-driven business. Cham: Springer, pp. 205–232. Available at: https://doi.org/10.1007/978-3-030-11821-1_12.
T. Stadelmann, V. Tolkachev, B. Sick, J. Stampfli, and O. Dürr, “Beyond ImageNet : deep learning in industrial practice,” in Applied data science : lessons learned for the data-driven business, M. Braschler, T. Stadelmann, and K. Stockinger, Eds. Cham: Springer, 2019, pp. 205–232. doi: 10.1007/978-3-030-11821-1_12.
STADELMANN, Thilo, Vasily TOLKACHEV, Beate SICK, Jan STAMPFLI und Oliver DÜRR, 2019. Beyond ImageNet : deep learning in industrial practice. In: Martin BRASCHLER, Thilo STADELMANN und Kurt STOCKINGER (Hrsg.), Applied data science : lessons learned for the data-driven business. Cham: Springer. S. 205–232. ISBN 978-3-030-11821-1
Stadelmann, Thilo, Vasily Tolkachev, Beate Sick, Jan Stampfli, and Oliver Dürr. 2019. “Beyond ImageNet : Deep Learning in Industrial Practice.” In Applied Data Science : Lessons Learned for the Data-Driven Business, edited by Martin Braschler, Thilo Stadelmann, and Kurt Stockinger, 205–32. Cham: Springer. https://doi.org/10.1007/978-3-030-11821-1_12.
Stadelmann, Thilo, et al. “Beyond ImageNet : Deep Learning in Industrial Practice.” Applied Data Science : Lessons Learned for the Data-Driven Business, edited by Martin Braschler et al., Springer, 2019, pp. 205–32, https://doi.org/10.1007/978-3-030-11821-1_12.


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