no code implementations • 6 May 2024 • Marek Herde, Lukas Lührs, Denis Huseljic, Bernhard Sick
Training with noisy class labels impairs neural networks' generalization performance.
1 code implementation • 13 Apr 2024 • Denis Huseljic, Paul Hahn, Marek Herde, Lukas Rauch, Bernhard Sick
BAIT, a recently proposed AL strategy based on the Fisher Information, has demonstrated impressive performance across various datasets.
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 • 5 Apr 2023 • Marek Herde, Denis Huseljic, Bernhard Sick
Training standard deep neural networks leads to subpar performances in such multi-annotator supervised learning settings.
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.
1 code implementation • 6 Oct 2022 • Denis Huseljic, Marek Herde, Mehmet Muejde, Bernhard Sick
In the field of deep learning based computer vision, the development of deep object detection has led to unique paradigms (e. g., two-stage or set-based) and architectures (e. g., Faster-RCNN or DETR) which enable outstanding performance on challenging benchmark datasets.
no code implementations • 23 Sep 2021 • Marek Herde, Denis Huseljic, Bernhard Sick, Adrian Calma
Therefore, we introduce a general real-world AL strategy as part of a learning cycle and use its elements, e. g., the query and annotator selection algorithm, to categorize about 60 real-world AL strategies.
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 • 16 May 2019 • Tom Hanika, Marek Herde, Jochen Kuhn, Jan Marco Leimeister, Paul Lukowicz, Sarah Oeste-Reiß, Albrecht Schmidt, Bernhard Sick, Gerd Stumme, Sven Tomforde, Katharina Anna Zweig
The field of collaborative interactive learning (CIL) aims at developing and investigating the technological foundations for a new generation of smart systems that support humans in their everyday life.