Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-3175
Title: Beyond ImageNet : deep learning in industrial practice
Authors : Stadelmann, Thilo
Tolkachev, Vasily
Sick, Beate
Stampfli, Jan
Dürr, Oliver
et. al : No
Published in : Applied data science : lessons learned for the data-driven business
Pages : 205
Pages to: 232
Editors of the parent work: Braschler, Martin
Stadelmann, Thilo
Stockinger, Kurt
Publisher / Ed. Institution : Springer
Publisher / Ed. Institution: Cham
Issue Date: 14-Jun-2019
License (according to publishing contract) : Licence according to publishing contract
Type of review: Editorial review
Language : English
Subjects : Deep learning; Convolutional neural network; Image recognition; Classification; Anomaly detection; speaker clustering; speaker recognition; CNN; Biomedical data analysis; Predictive maintenance; Fully convolutional neural network; Document analysis
Subject (DDC) : 004: Computer science
Abstract: Deep 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).
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Publication type: Book Part
DOI : 10.21256/zhaw-3175
10.1007/978-3-030-11821-1_12
ISBN: 978-3-030-11821-1
978-3-030-11820-4
URI: https://digitalcollection.zhaw.ch/handle/11475/17425
Published as part of the ZHAW project : DaCoMo - Data-Driven Condition Monitoring
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Appears in Collections:Publikationen School of Engineering

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