34 citations · 85 across the 11 of their papers we have counts for
Showing 2020Show all
3 papers · 1 filter
cs.LG2020★ 9 cited
Generating Artificial Outliers in the Absence of Genuine Ones -- a Survey
Georg Steinbuss, Klemens Böhm
By definition, outliers are rarely observed in reality, making them difficult to detect or analyse. Artificial outliers approximate such genuine outliers and can, for instance, hel…
cs.LG2020★ 34 cited
Incremental Real-Time Personalization in Human Activity Recognition Using Domain Adaptive Batch Normalization
Alan Mazankiewicz, Klemens Böhm, Mario Bergés
Human Activity Recognition (HAR) from devices like smartphone accelerometers is a fundamental problem in ubiquitous computing. Machine learning based recognition models often perfo…
cs.LG2020★ 33 cited
Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Data
Georg Steinbuss, Klemens Böhm
Benchmarking unsupervised outlier detection is difficult. Outliers are rare, and existing benchmark data contains outliers with various and unknown characteristics. Fully synthetic…