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https://doi.org/10.21256/zhaw-3175| Publication type: | Book part |
| Type of review: | Editorial review |
| Title: | Beyond ImageNet : deep learning in industrial practice |
| Authors: | Stadelmann, Thilo Tolkachev, Vasily Sick, Beate Stampfli, Jan Dürr, Oliver |
| et. al: | No |
| DOI: | 10.1007/978-3-030-11821-1_12 10.21256/zhaw-3175 |
| Published in: | Applied data science : lessons learned for the data-driven business |
| Editors of the parent work: | Braschler, Martin Stadelmann, Thilo Stockinger, Kurt |
| Page(s): | 205 |
| Pages to: | 232 |
| Issue Date: | 14-Jun-2019 |
| Publisher / Ed. Institution: | Springer |
| Publisher / Ed. Institution: | Cham |
| ISBN: | 978-3-030-11821-1 978-3-030-11820-4 |
| 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): | 006: Special computer methods |
| 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). |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/17425 |
| Fulltext version: | Submitted version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Computer Science (InIT) |
| Published as part of the ZHAW project: | DaCoMo - Data-Driven Condition Monitoring PANOPTES |
| Appears in collections: | Publikationen School of Engineering |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ADS_2019_DeepLearning.pdf | preprint | 1.52 MB | Adobe PDF |  View/Open |
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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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