no code implementations • 17 May 2024 • Nisha L. Raichur, Lucas Heublein, Tobias Feigl, Alexander Rügamer, Christopher Mutschler, Felix Ott
The primary objective of methods in continual learning is to learn tasks in a sequential manner over time from a stream of data, while mitigating the detrimental phenomenon of catastrophic forgetting.
no code implementations • 9 Feb 2024 • Felix Ott, Lucas Heublein, Nisha Lakshmana Raichur, Tobias Feigl, Jonathan Hansen, Alexander Rügamer, Christopher Mutschler
We recorded a dataset at a motorway with eight interference classes on which our FSL method with quadruplet loss outperforms other FSL techniques in jammer classification accuracy with 97. 66%.
no code implementations • 14 Apr 2023 • Felix Ott, Lucas Heublein, David Rügamer, Bernd Bischl, Christopher Mutschler
In this work, we propose recurrent fusion networks to optimally align absolute and relative pose predictions to improve the absolute pose prediction.
no code implementations • 16 Jan 2023 • Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler
The goal of domain adaptation (DA) is to mitigate this domain shift problem by searching for an optimal feature transformation to learn a domain-invariant representation.
no code implementations • 1 Aug 2022 • Felix Ott, Nisha Lakshmana Raichur, David Rügamer, Tobias Feigl, Heiko Neumann, Bernd Bischl, Christopher Mutschler
We show accuracy improvements for the APR-RPR task and for the RPR-RPR task for aerial vehicles and hand-held devices.
no code implementations • 17 Jun 2022 • Andreas Klaß, Sven M. Lorenz, Martin W. Lauer-Schmaltz, David Rügamer, Bernd Bischl, Christopher Mutschler, Felix Ott
For many applications, analyzing the uncertainty of a machine learning model is indispensable.
1 code implementation • 7 Apr 2022 • Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler
To mitigate this domain shift problem, domain adaptation (DA) techniques search for an optimal transformation that converts the (current) input data from a source domain to a target domain to learn a domain-invariant representation that reduces domain discrepancy.
no code implementations • 16 Feb 2022 • Felix Ott, David Rügamer, Lucas Heublein, Bernd Bischl, Christopher Mutschler
We perform extensive evaluations on synthetic image and time-series data, and on data for offline handwriting recognition (HWR) and on online HWR from sensor-enhanced pens for classifying written words.
no code implementations • 14 Feb 2022 • Felix Ott, David Rügamer, Lucas Heublein, Tim Hamann, Jens Barth, Bernd Bischl, Christopher Mutschler
While there exist many offline HWR datasets, there is only little data available for the development of OnHWR methods on paper as it requires hardware-integrated pens.
no code implementations • 17 Dec 2019 • Felix Ott, Tobias Feigl, Christoffer Löffler, Christopher Mutschler
Visual Odometry (VO) accumulates a positional drift in long-term robot navigation tasks.