no code implementations • 12 Sep 2023 • Tuan Pham Minh, Jayan Wijesingha, Daniel Kottke, Marek Herde, Denis Huseljic, Bernhard Sick, Michael Wachendorf, Thomas Esch
Since these initial labels are partially erroneous, we use active learning strategies to cost-efficiently refine the labels in the second step.
1 code implementation • 12 Oct 2022 • Marek Herde, Zhixin Huang, Denis Huseljic, Daniel Kottke, Stephan Vogt, Bernhard Sick
Retraining deep neural networks when new data arrives is typically computationally expensive.
no code implementations • 9 Aug 2021 • Daniel Kottke, Georg Krempl, Marianne Stecklina, Cornelius Styp von Rekowski, Tim Sabsch, Tuan Pham Minh, Matthias Deliano, Myra Spiliopoulou, Bernhard Sick
In machine learning, active class selection (ACS) algorithms aim to actively select a class and ask the oracle to provide an instance for that class to optimize a classifier's performance while minimizing the number of requests.
2 code implementations • 29 Sep 2020 • Adrian Englhardt, Holger Trittenbach, Daniel Kottke, Bernhard Sick, Klemens Böhm
Our approach is to frame SVDD sampling as an optimization problem, where constraints guarantee that sampling indeed approximates the original decision boundary.
1 code implementation • 2 Jun 2020 • Daniel Kottke, Marek Herde, Christoph Sandrock, Denis Huseljic, Georg Krempl, Bernhard Sick
Gathering labeled data to train well-performing machine learning models is one of the critical challenges in many applications.
no code implementations • 29 Jan 2019 • Daniel Kottke, Jim Schellinger, Denis Huseljic, Bernhard Sick
Hence, it is not possible to reliably estimate the performance of the classification system during learning and it is difficult to decide when the system fulfills the quality requirements (stopping criteria).