Please use this identifier to cite or link to this item: http://dx.doi.org/10.18419/opus-10882
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dc.contributor.authorMaschler, Benjamin-
dc.contributor.authorKamm, Simon-
dc.contributor.authorNasser, Jazdi-
dc.contributor.authorWeyrich, Michael-
dc.date.accessioned2020-06-08T07:55:23Z-
dc.date.available2020-06-08T07:55:23Z-
dc.date.issued2020de
dc.identifier.urihttp://elib.uni-stuttgart.de/handle/11682/10899-
dc.identifier.urihttp://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-108998de
dc.identifier.urihttp://dx.doi.org/10.18419/opus-10882-
dc.description.abstractIn this paper, a novel light-weight incremental class learning algorithm for live image recognition is presented. It features a dual memory architecture and is capable of learning formerly unknown classes as well as conducting its learning across multiple instances at multiple locations without storing any images. In addition to tests on the ImageNet dataset, a prototype based upon a Raspberry Pi and a webcam is used for further evaluation: The proposed algorithm successfully allows for the performant execution of image classification tasks while learning new classes at several sites simultaneously, thereby enabling its application to various industry use cases, e.g. predictive maintenance or self-optimization.en
dc.language.isoende
dc.rightsinfo:eu-repo/semantics/openAccessde
dc.subject.ddc004de
dc.subject.ddc620de
dc.subject.ddc621.3de
dc.titleDistributed cooperative deep transfer learning for industrial image recognitionen
dc.typepreprintde
ubs.fakultaetInformatik, Elektrotechnik und Informationstechnikde
ubs.institutInstitut für Automatisierungstechnik und Softwaresystemede
ubs.publikation.noppnyesde
ubs.publikation.seiten6de
ubs.publikation.typPreprintde
Appears in Collections:05 Fakultät Informatik, Elektrotechnik und Informationstechnik

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