1 code implementation • 4 May 2024 • M. Saquib Sarfraz, Mei-Yen Chen, Lukas Layer, Kunyu Peng, Marios Koulakis
The current state of machine learning scholarship in Timeseries Anomaly Detection (TAD) is plagued by the persistent use of flawed evaluation metrics, inconsistent benchmarking practices, and a lack of proper justification for the choices made in novel deep learning-based model designs.
no code implementations • 10 Apr 2022 • Alina Roitberg, Kunyu Peng, David Schneider, Kailun Yang, Marios Koulakis, Manuel Martinez, Rainer Stiefelhagen
In this work, we for the first time examine how well the confidence values of modern driver observation models indeed match the probability of the correct outcome and show that raw neural network-based approaches tend to significantly overestimate their prediction quality.
1 code implementation • CVPR 2022 • M. Saquib Sarfraz, Marios Koulakis, Constantin Seibold, Rainer Stiefelhagen
Dimensionality reduction is crucial both for visualization and preprocessing high dimensional data for machine learning.
1 code implementation • 30 Nov 2021 • Kunyu Peng, Alina Roitberg, David Schneider, Marios Koulakis, Kailun Yang, Rainer Stiefelhagen
Human affect recognition is a well-established research area with numerous applications, e. g., in psychological care, but existing methods assume that all emotions-of-interest are given a priori as annotated training examples.