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http://dx.doi.org/10.18419/opus-10882
Autor(en): | Maschler, Benjamin Kamm, Simon Nasser, Jazdi Weyrich, Michael |
Titel: | Distributed cooperative deep transfer learning for industrial image recognition |
Erscheinungsdatum: | 2020 |
Dokumentart: | Preprint |
Seiten: | 6 |
URI: | http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-108998 http://elib.uni-stuttgart.de/handle/11682/10899 http://dx.doi.org/10.18419/opus-10882 |
Zusammenfassung: | In 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. |
Enthalten in den Sammlungen: | 05 Fakultät Informatik, Elektrotechnik und Informationstechnik |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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Distributed Cooperative Deep Transfer Learning for Industrial Image Recognition.pdf | 519,23 kB | Adobe PDF | Öffnen/Anzeigen |
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