Conditional Dependence Tests Reveal the Usage of ABCD Rule Features and Bias Variables in Automatic Skin Lesion Classification. - Christian Reimers; Niklas Penzel; Paul Bodesheim; Jakob Runge; Joachim Denzler (CVPR Workshops, 2021)

Investigating the Consistency of Uncertainty Sampling in Deep Active Learning. - Niklas Penzel; Christian Reimers; Clemens-Alexander Brust; Joachim Denzler (GCPR, 2021)

Facing Asymmetry - Uncovering the Causal Link Between Facial Symmetry and Expression Classifiers Using Synthetic Interventions. - Tim Büchner; Niklas Penzel; Orlando Guntinas-Lichius; Joachim Denzler (ACCV (4), 2022)

Investigating Neural Network Training on a Feature Level Using Conditional Independence. - Niklas Penzel; Christian Reimers; Paul Bodesheim; Joachim Denzler (ECCV Workshops (6), 2022)

Beyond Debiasing: Actively Steering Feature Selection via Loss Regularization. - Jan Blunk; Niklas Penzel; Paul Bodesheim; Joachim Denzler (DAGM, 2023)

Analyzing the Behavior of Cauliflower Harvest-Readiness Models by Investigating Feature Relevances. - Niklas Penzel; Jana Kierdorf; Ribana Roscher; Joachim Denzler (ICCV (Workshops), 2023)

Interpreting Art by Leveraging Pre-Trained Models. - Niklas Penzel; Joachim Denzler (MVA, 2023)

The Power of Properties: Uncovering the Influential Factors in Emotion Classification. - Tim Büchner; Niklas Penzel; Orlando Guntinas-Lichius; Joachim Denzler (ICPRAI (2), 2024)

Reducing Bias in Pre-Trained Models by Tuning While Penalizing Change. - Niklas Penzel; Gideon Stein; Joachim Denzler (VISIGRAPP (2): VISAPP, 2024)

When Medical Imaging Met Self-Attention: A Love Story That Didn't Quite Work out. - Tristan Piater; Niklas Penzel; Gideon Stein; Joachim Denzler (VISIGRAPP (2): VISAPP, 2024)

CausalRivers - Scaling up benchmarking of causal discovery for real-world time-series. - Gideon Stein; Maha Shadaydeh; Jan Blunk; Niklas Penzel; Joachim Denzler (ICLR, 2025)

Towards Locally Explaining Prediction Behavior via Gradual Interventions and Measuring Property Gradients. - Niklas Penzel; Joachim Denzler (CoRR, 2025)

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks. - Laines Schmalwasser; Niklas Penzel; Joachim Denzler; Julia Niebling (CoRR, 2025)