Niklas Penzel, M.Sc.
Niklas Penzel
Address:Computer Vision Group
 Department of Mathematics and Computer Science
 Friedrich Schiller University of Jena
 Ernst-Abbe-Platz 2
 07743 Jena
Phone:+49 (0) 3641 9 46335
E-mail:niklas (dot) penzel (at) uni-jena (dot) de
Links:Google Scholar
Curriculum Vitae
Since Dec. 2020Research Associate at the Computer Vision Group, Friedrich Schiller University Jena
2020Master Thesis: “The Bias Uncertainty Sampling introduces into an Active Learning System”
2018-2020M.Sc. in Computer Science at the Friedrich Schiller University Jena
2018Bachelor Thesis: “Lebenslanges Lernen von Klassifikationssystemen ohne Vorwissen und mit intelligenter Datenhaltung”
(Lifelong Learning of Classification Systems without Previous Knowledge and with Smart Data Management)
2015-2018B.Sc. in Computer Science at the Friedrich Schiller University Jena
Research Interests
  • Explainable AI
  • Analyzing Model Training
  • Lifelong Learning
  • Deep Learning
  • Super Resolution
Supervised Thesis
  • Jan Blunk: “Steering Feature Usage During Neural Network Model Training”. Master thesis, 2023. (joint supervision with Paul Bodesheim)
  • Tristan Piater: “Self-Attention Mechanisms for the Classification of Dermoscopic Images”. Bachelor thesis, 2022 (joint supervision with Gideon Stein)
  • Maria Gogolev: “Continual fine-tuning with intelligent rehearsal selection”. Bachelor thesis, 2022 (joint supervision with Julia Böhlke)
Jan Blunk, Niklas Penzel, Paul Bodesheim, Joachim Denzler:
Beyond Debiasing: Actively Steering Feature Selection via Loss Regularization.
DAGM German Conference on Pattern Recognition (DAGM-GCPR). 2023.
[bibtex] [pdf] [abstract]
Niklas Penzel, Jana Kierdorf, Ribana Roscher, Joachim Denzler:
Analyzing the Behavior of Cauliflower Harvest-Readiness Models by Investigating Feature Relevances.
ICCV Workshop on Computer Vision in Plant Phenotyping and Agriculture (CVPPA). 2023.
[bibtex] [pdf] [abstract]
Niklas Penzel, Joachim Denzler:
Interpreting Art by Leveraging Pre-Trained Models.
International Conference on Machine Vision and Applications (MVA). Pages 1-6. 2023.
[bibtex] [doi] [abstract]
Niklas Penzel, Christian Reimers, Paul Bodesheim, Joachim Denzler:
Investigating Neural Network Training on a Feature Level using Conditional Independence.
ECCV Workshop on Causality in Vision (ECCV-WS). Pages 383-399. 2022.
[bibtex] [pdf] [abstract]
Christian Reimers, Niklas Penzel, Paul Bodesheim, Jakob Runge, Joachim Denzler:
Conditional Dependence Tests Reveal the Usage of ABCD Rule Features and Bias Variables in Automatic Skin Lesion Classification.
CVPR ISIC Skin Image Analysis Workshop (CVPR-WS). Pages 1810-1819. 2021.
[bibtex] [pdf] [web] [abstract]
Niklas Penzel, Christian Reimers, Clemens-Alexander Brust, Joachim Denzler:
Investigating the Consistency of Uncertainty Sampling in Deep Active Learning.
DAGM German Conference on Pattern Recognition (DAGM-GCPR). Pages 159-173. 2021.
[bibtex] [pdf] [web] [doi] [abstract]