Please use this identifier to cite or link to this item:
|Publication type:||Conference paper|
|Type of review:||Peer review (publication)|
|Title:||Deep learning in the wild|
Duivesteijn, Gilbert François
Meier, Benjamin Bruno
|Proceedings:||Artificial Neural Networks in Pattern Recognition|
|Conference details:||8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR), Siena, Italy, 19-21 September 2018|
|Series:||Lecture Notes in Computer Science|
|Publisher / Ed. Institution:||Springer|
|Subjects:||Data availability; Deployment; Loss & reward shaping; Real world tasks|
|Subject (DDC):||006: Special computer methods|
|Abstract:||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.|
|Further description:||Invited paper|
|Fulltext version:||Accepted version|
|License (according to publishing contract):||Licence according to publishing contract|
|Departement:||School of Engineering|
|Organisational Unit:||Institute of Applied Information Technology (InIT)|
|Published as part of the ZHAW project:||Libra: A One-Tool Solution for MLD4 Compliance|
|Appears in collections:||Publikationen School of Engineering|
Files in This Item:
|ANNPR_2018d.pdf||Accepted Version||4.87 MB||Adobe PDF|
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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, 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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