L
Lukas Tuggener
Researcher at Dalle Molle Institute for Artificial Intelligence Research
Publications - 17
Citations - 189
Lukas Tuggener is an academic researcher from Dalle Molle Institute for Artificial Intelligence Research. The author has contributed to research in topics: Deep learning & Optical music recognition. The author has an hindex of 6, co-authored 15 publications receiving 120 citations. Previous affiliations of Lukas Tuggener include Zurich University of Applied Sciences/ZHAW & Winterthur Museum, Garden and Library.
Papers
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Proceedings ArticleDOI
Automated Machine Learning in Practice: State of the Art and Recent Results
Lukas Tuggener,Mohammadreza Amirian,Katharina Rombach,Stefan Lörwald,Anastasia Varlet,Christian Westermann,Thilo Stadelmann +6 more
TL;DR: An overview of the state of the art in AutoML with a focus on practical applicability in a business context, and recent benchmark results of the most important AutoML algorithms are provided in this article.
Book ChapterDOI
Deep Learning in the Wild
Thilo Stadelmann,Mohammadreza Amirian,Ismail Arabaci,Marek Arnold,Gilbert François Duivesteijn,Ismail Elezi,Melanie Geiger,Stefan Lörwald,Benjamin Bruno Meier,Katharina Rombach,Lukas Tuggener +10 more
TL;DR: This paper explored 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. But they did not provide any guidance on how to make them work in practice.
Proceedings ArticleDOI
Deep watershed detector for music object recognition
TL;DR: Deep Watershed Detector (DWD) as discussed by the authors is a novel object detection method based on synthetic energy maps and watershed transform, which is specifically tailored to deal with high resolution images that contain a large number of very small objects and is therefore able to process full pages of written music.
Posted Content
Deep Learning in the Wild
Thilo Stadelmann,Mohammadreza Amirian,Ismail Arabaci,Marek Arnold,Gilbert François Duivesteijn,Ismail Elezi,Melanie Geiger,Stefan Lörwald,Benjamin Bruno Meier,Katharina Rombach,Lukas Tuggener +10 more
TL;DR: Insight is given 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.
Posted Content
DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny Objects
TL;DR: A detailed statistical analysis of the DeepScores dataset is presented, comparing it with other computer vision datasets like PASCAL VOC, SUN, SVHN, ImageNet, MS-COCO, as well as with other OMR datasets.