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https://doi.org/10.21256/zhaw-3872| Publikationstyp: | Konferenz: Paper |
| Art der Begutachtung: | Peer review (Publikation) |
| Titel: | Deep learning in the wild |
| Autor/-in: | Stadelmann, Thilo Amirian, Mohammadreza Arabaci, Ismail Arnold, Marek Duivesteijn, Gilbert François Elezi, Ismail Geiger, Melanie Lörwald, Stefan Meier, Benjamin Bruno Rombach, Katharina Tuggener, Lukas |
| DOI: | 10.1007/978-3-319-99978-4_2 10.21256/zhaw-3872 |
| Tagungsband: | Artificial Neural Networks in Pattern Recognition |
| Seite(n): | 17 |
| Seiten bis: | 38 |
| Angaben zur Konferenz: | 8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR), Siena, Italy, 19-21 September 2018 |
| Erscheinungsdatum: | 2018 |
| Reihe: | Lecture Notes in Computer Science |
| Reihenzählung: | 11081 |
| Verlag / Hrsg. Institution: | Springer |
| ISBN: | 978-3-319-99977-7 978-3-319-99978-4 |
| Sprache: | Englisch |
| Schlagwörter: | Data availability; Deployment; Loss & reward shaping; Real world tasks |
| Fachgebiet (DDC): | 006: Spezielle Computerverfahren |
| Zusammenfassung: | Deep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks. While this interest is fueled by beautiful success stories, practical work in deep learning on novel tasks without existing baselines remains challenging. This paper explores the specific challenges arising in the realm of real world tasks, based on case studies from research & development in conjunction with industry, and extracts lessons learned from them. It thus fills a gap between the publication of latest algorithmic and methodical developments, and the usually omitted nitty-gritty of how to make them work. Specifically, we give insight into deep learning projects on face matching, print media monitoring, industrial quality control, music scanning, strategy game playing, and automated machine learning, thereby providing best practices for deep learning in practice. |
| Weitere Angaben: | Invited paper |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/8131 |
| Volltext Version: | Akzeptierte Version |
| Lizenz (gemäss Verlagsvertrag): | Lizenz gemäss Verlagsvertrag |
| Departement: | School of Engineering |
| Organisationseinheit: | Institut für Informatik (InIT) |
| Publiziert im Rahmen des ZHAW-Projekts: | Libra: A One-Tool Solution for MLD4 Compliance |
| Enthalten in den Sammlungen: | Publikationen School of Engineering |
Dateien zu dieser Ressource:
| Datei | Beschreibung | Größe | Format | |
|---|---|---|---|---|
| ANNPR_2018d.pdf | Accepted Version | 4.87 MB | Adobe PDF |  Öffnen/Anzeigen |
Zur Langanzeige
Stadelmann, T., Amirian, M., Arabaci, I., Arnold, M., Duivesteijn, G. F., Elezi, I., Geiger, M., Lörwald, S., Meier, B. B., Rombach, K., & Tuggener, L. (2018). Deep learning in the wild [Conference paper]. Artificial Neural Networks in Pattern Recognition, 17–38. https://doi.org/10.1007/978-3-319-99978-4_2
Stadelmann, T. et al. (2018) ‘Deep learning in the wild’, in Artificial Neural Networks in Pattern Recognition. Springer, pp. 17–38. Available at: https://doi.org/10.1007/978-3-319-99978-4_2.
T. Stadelmann et al., “Deep learning in the wild,” in Artificial Neural Networks in Pattern Recognition, 2018, pp. 17–38. doi: 10.1007/978-3-319-99978-4_2.
STADELMANN, Thilo, Mohammadreza AMIRIAN, Ismail ARABACI, Marek ARNOLD, Gilbert François DUIVESTEIJN, Ismail ELEZI, Melanie GEIGER, Stefan LÖRWALD, Benjamin Bruno MEIER, Katharina ROMBACH und Lukas TUGGENER, 2018. Deep learning in the wild. In: Artificial Neural Networks in Pattern Recognition. Conference paper. Springer. 2018. S. 17–38. ISBN 978-3-319-99977-7
Stadelmann, Thilo, Mohammadreza Amirian, Ismail Arabaci, Marek Arnold, Gilbert François Duivesteijn, Ismail Elezi, Melanie Geiger, et al. 2018. “Deep Learning in the Wild.” Conference paper. In Artificial Neural Networks in Pattern Recognition, 17–38. Springer. https://doi.org/10.1007/978-3-319-99978-4_2.
Stadelmann, Thilo, et al. “Deep Learning in the Wild.” Artificial Neural Networks in Pattern Recognition, Springer, 2018, pp. 17–38, https://doi.org/10.1007/978-3-319-99978-4_2.
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