Distributed Cooperative Deep Transfer Learning for Industrial Image Recognition

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 stor...

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Bibliographic Details
Published inProcedia CIRP Vol. 93; pp. 437 - 442
Main Authors Maschler, Benjamin, Kamm, Simon, Jazdi, Nasser, Weyrich, Michael
Format Journal Article
LanguageEnglish
Published Elsevier B.V 2020
Subjects
Artificial Intelligence
Artificial Neural Networks
Continual Learning
Deep Learning
Distributed Learning
Dual Memory Method
Incremental Class Learning
Live Image Recognition
Transfer Learning
Continual Learning
Transfer Learning
Artificial Neural Networks
Artificial Intelligence
Live Image Recognition
Distributed Learning
Incremental Class Learning
Deep Learning
Dual Memory Method
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Summary: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.
ISSN:2212-8271
2212-8271
DOI:10.1016/j.procir.2020.03.056