Optimal Reliability in Design for Fatigue Life. - Hanno Gottschalk; Sebastian Schmitz (SIAM J. Control. Optim., 2014)

Minimal Failure Probability for Ceramic Design Via Shape Control. - Matthias Bolten; Hanno Gottschalk; Sebastian Schmitz (J. Optim. Theory Appl., 2015)

Classification Uncertainty of Deep Neural Networks Based on Gradient Information. - Philipp Oberdiek; Matthias Rottmann; Hanno Gottschalk (ANNPR, 2018)

Deep Bayesian Active Semi-Supervised Learning. - Matthias Rottmann; Karsten Kahl; Hanno Gottschalk (ICMLA, 2018)

Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities. - Matthias Rottmann; Pascal Colling; Thomas-Paul Hack; Fabian Hüger; Peter Schlicht; Hanno Gottschalk (CoRR, 2018)

Calibration of léVY Processes using Optimal control of Kolmogorov equations with periodic boundary conditions. - Mario Annunziato; Hanno Gottschalk (Math. Model. Anal., 2018)

The Ethical Dilemma When (Not) Setting up Cost-Based Decision Rules in Semantic Segmentation. - Robin Chan; Matthias Rottmann; Radin Dardashti; Fabian Hüger; Peter Schlicht; Hanno Gottschalk (CVPR Workshops, 2019)

Numerical shape optimization to decrease failure probability of ceramic structures. - Matthias Bolten; Hanno Gottschalk; Camilla Hahn; Mohamed Saadi (Comput. Vis. Sci., 2019)

Application of Decision Rules for Handling Class Imbalance in Semantic Segmentation. - Robin Chan; Matthias Rottmann; Fabian Hüger; Peter Schlicht; Hanno Gottschalk (CoRR, 2019)

Using adjoint CFD to quantify the impact of manufacturing variations on a heavy duty turbine vane. - Alexander Liefke; Vincent Marciniak; Uwe Janoske; Hanno Gottschalk (CoRR, 2019)

MetaFusion: Controlled False-Negative Reduction of Minority Classes in Semantic Segmentation. - Robin Chan; Matthias Rottmann; Fabian Hüger; Peter Schlicht; Hanno Gottschalk (CoRR, 2019)

Detection of False Positive and False Negative Samples in Semantic Segmentation. - Matthias Rottmann; Kira Maag; Robin Chan; Fabian Hüger; Peter Schlicht; Hanno Gottschalk (DATE, 2020)

Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks. - Kira Maag; Matthias Rottmann; Hanno Gottschalk (ICTAI, 2020)

Controlled False Negative Reduction of Minority Classes in Semantic Segmentation. - Robin Chan; Matthias Rottmann; Fabian Hüger; Peter Schlicht; Hanno Gottschalk (IJCNN, 2020)

Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities. - Matthias Rottmann; Pascal Colling; Thomas-Paul Hack; Robin Chan; Fabian Hüger; Peter Schlicht; Hanno Gottschalk (IJCNN, 2020)

GivEn - Shape Optimization for Gas Turbines in Volatile Energy Networks. - Jan Backhaus; Matthias Bolten; Onur Tanil Doganay; Matthias Ehrhardt 001; Benedikt Engel; Christian Frey; Hanno Gottschalk; Michael Günther 005; Camilla Hahn; Jens Jäschke; Peter Jaksch; Kathrin Klamroth; et al. (CoRR, 2020)

Coarsening in Algebraic Multigrid using Gaussian Processes. - Hanno Gottschalk; Karsten Kahl (CoRR, 2020)

Detection of Iterative Adversarial Attacks via Counter Attack. - Matthias Rottmann; Mathis Peyron; Natasa Krejic; Hanno Gottschalk (CoRR, 2020)

A Convenient Infinite Dimensional Framework for Generative Adversarial Learning. - Hayk Asatryan; Hanno Gottschalk; Marieke Lippert; Matthias Rottmann (CoRR, 2020)

Improving Video Instance Segmentation by Light-weight Temporal Uncertainty Estimates. - Kira Maag; Matthias Rottmann; Fabian Hüger; Peter Schlicht; Hanno Gottschalk (CoRR, 2020)

YOdar: Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors. - Kamil Kowol; Matthias Rottmann; Stefan Bracke; Hanno Gottschalk (ICAART (2), 2021)

Entropy Maximization and Meta Classification for Out-of-Distribution Detection in Semantic Segmentation. - Robin Chan; Matthias Rottmann; Hanno Gottschalk (ICCV, 2021)

MetaBox+: A New Region based Active Learning Method for Semantic Segmentation using Priority Maps. - Pascal Colling; Lutz Roese-Koerner; Hanno Gottschalk; Matthias Rottmann (ICPRAM, 2021)

False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation. - Pascal Colling; Matthias Rottmann; Lutz Roese-Koerner; Hanno Gottschalk (ICTAI, 2021)

Improving Video Instance Segmentation by Light-weight Temporal Uncertainty Estimates. - Kira Maag; Matthias Rottmann; Serin Varghese; Fabian Hüger; Peter Schlicht; Hanno Gottschalk (IJCNN, 2021)

Background-Foreground Segmentation for Interior Sensing in Automotive Industry. - Claudia Drygala; Matthias Rottmann; Hanno Gottschalk; Klaus Friedrichs; Thomas Kurbiel (CoRR, 2021)

Generative Modeling of Turbulence. - Claudia Drygala; Benjamin Winhart; Francesca di Mare; Hanno Gottschalk (CoRR, 2021)

Does Redundancy in AI Perception Systems Help to Test for Super-Human Automated Driving Performance? - Hanno Gottschalk; Matthias Rottmann; Maida Saltagic (CoRR, 2021)

An Analytical Study in Multi-physics and Multi-criteria Shape Optimization. - Hanno Gottschalk; Marco Reese (J. Optim. Theory Appl., 2021)

Spontaneous Wave Formation in Stochastic Self-Driven Particle Systems. - Martin Friesen; Hanno Gottschalk; Barbara Rüdiger; Antoine Tordeux (SIAM J. Appl. Math., 2021)

Tracing Locally Pareto-Optimal Points by Numerical Integration. - Matthias Bolten; Onur Tanil Doganay; Hanno Gottschalk; Kathrin Klamroth (SIAM J. Control. Optim., 2021)

Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects. - Kira Maag; Robin Chan; Svenja Uhlemeyer; Kamil Kowol; Hanno Gottschalk (ACCV (5), 2022)

A-Eye: Driving with the Eyes of AI for Corner Case Generation. - Kamil Kowol; Stefan Bracke; Hanno Gottschalk (CHIRA, 2022)

survAIval: Survival Analysis with the Eyes of AI. - Kamil Kowol; Stefan Bracke; Hanno Gottschalk (CHIRA (Revised Selected Papers), 2022)

Towards unsupervised open world semantic segmentation. - Svenja Uhlemeyer; Matthias Rottmann; Hanno Gottschalk (UAI, 2022)

Detecting and Learning the Unknown in Semantic Segmentation. - Robin Chan; Svenja Uhlemeyer; Matthias Rottmann; Hanno Gottschalk (CoRR, 2022)

Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning. - Julian Burghoff; Robin Chan; Hanno Gottschalk; Annika Mütze; Tobias Riedlinger; Matthias Rottmann; Marius Schubert (CoRR, 2022)

Semi-supervised domain adaptation with CycleGAN guided by a downstream task loss. - Annika Mütze; Matthias Rottmann; Hanno Gottschalk (CoRR, 2022)

Temporal Performance Prediction for Deep Convolutional Long Short-Term Memory Networks. - Laura Fieback; Bidya Dash; Jakob Spiegelberg; Hanno Gottschalk (AALTD@ECML/PKDD, 2023)

Risk Stratification of Malignant Melanoma Using Neural Networks. - Julian Burghoff; Leonhard Ackermann; Younes Salahdine; Veronika Bram; Katharina Wunderlich; Julius Balkenhol; Thomas Dirschka; Hanno Gottschalk (ICANN (4), 2023)

Who Breaks Early, Looses: Goal Oriented Training of Deep Neural Networks Based on Port Hamiltonian Dynamics. - Julian Burghoff; Marc Heinrich Monells; Hanno Gottschalk (ICANN (10), 2023)

LU-Net: Invertible Neural Networks Based on Matrix Factorization. - Robin Chan; Sarina Penquitt; Hanno Gottschalk (IJCNN, 2023)

Augmentation-based Domain Generalization for Semantic Segmentation. - Manuel Schwonberg; Fadoua El Bouazati; Nico M. Schmidt; Hanno Gottschalk (IV, 2023)

Semi-Supervised Domain Adaptation with CycleGAN Guided by Downstream Task Awareness. - Annika Mütze; Matthias Rottmann; Hanno Gottschalk (VISIGRAPP (5: VISAPP), 2023)

Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors. - Tobias Riedlinger; Matthias Rottmann; Marius Schubert; Hanno Gottschalk (WACV, 2023)

Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving. - Manuel Schwonberg; Joshua Niemeijer; Jan-Aike Termöhlen; Jörg P. Schäfer; Nico M. Schmidt; Hanno Gottschalk; Tim Fingscheidt (IEEE Access, 2023)

Equivariant and Steerable Neural Networks: A review with special emphasis on the symmetric group. - Patrick Krüger; Hanno Gottschalk (CoRR, 2023)

Ridepooling and public bus services: A comparative case-study. - Hayk Asatryan; Daniela Gaul; Hanno Gottschalk; Kathrin Klamroth; Michael Stiglmayr (CoRR, 2023)

Generalization capabilities of conditional GAN for turbulent flow under changes of geometry. - Claudia Drygala; Francesca di Mare; Hanno Gottschalk (CoRR, 2023)

Non-convex shape optimization by dissipative Hamiltonian flows. - Matthias Bolten; Onur Tanil Doganay; Hanno Gottschalk; Kathrin Klamroth (CoRR, 2023)

Detecting Novelties with Empty Classes. - Svenja Uhlemeyer; Julian Lienen; Eyke Hüllermeier; Hanno Gottschalk (CoRR, 2023)

VLTSeg: Simple Transfer of CLIP-Based Vision-Language Representations for Domain Generalized Semantic Segmentation. - Christoph Hümmer; Manuel Schwonberg; Liangwei Zhou; Hu Cao; Alois Knoll; Hanno Gottschalk (CoRR, 2023)

Prediction Quality Meta Regression and Error Meta Classification for Segmented Lidar Point Clouds. - Pascal Colling; Matthias Rottmann; Lutz Roese-Koerner; Hanno Gottschalk (Int. J. Artif. Intell. Tools, 2023)

Detection of Iterative Adversarial Attacks via Counter Attack. - Matthias Rottmann; Kira Maag; Mathis Peyron; Hanno Gottschalk; Natasa Krejic (J. Optim. Theory Appl., 2023)

What should AI see? Using the public's opinion to determine the perception of an AI. - Robin Chan; Radin Dardashti; Meike Osinski; Matthias Rottmann; Dominik Brüggemann; Cilia Rücker; Peter Schlicht; Fabian Hüger; Nikol Rummel; Hanno Gottschalk (AI Ethics, 2023)

Strong but Simple: A Baseline for Domain Generalized Dense Perception by CLIP-Based Transfer Learning. - Christoph Hümmer; Manuel Schwonberg; Liangwei Zhou; Hu Cao; Alois Knoll; Hanno Gottschalk (ACCV (10), 2024)

A Study on Unsupervised Domain Adaptation for Semantic Segmentation in the Era of Vision-Language Models. - Manuel Schwonberg; Claus Werner; Hanno Gottschalk; Carsten Meyer (BMVC Workshops, 2024)

Unsupervised Class Incremental Learning using Empty Classes. - Svenja Uhlemeyer; Julian Lienen; Youssef Shoeb; Eyke Hüllermeier; Hanno Gottschalk (BMVC Workshops, 2024)

AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving. - Daniel Bogdoll; Iramm Hamdard; Lukas Namgyu Rößler; Felix Geisler; Muhammed Bayram; Felix Wang; Jan Imhof; Miguel de Campos; Anushervon Tabarov; Yitian Yang; Martin Gontscharow; Hanno Gottschalk; et al. (ECCV Workshops (7), 2024)

ResBuilder: Automated Learning of Depth with Residual Structures. - Julian Burghoff; Matthias Rottmann; Jill von Conta; Sebastian Schoenen; Andreas Witte; Hanno Gottschalk (ICANN (1), 2024)

Hyperparameter Optimization for Driving Strategies Based on Reinforcement Learning. - Nihal Acharya Adde; Hanno Gottschalk; Andreas Ebert (LOD (2), 2024)

Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection. - Tobias Riedlinger; Marius Schubert; Karsten Kahl; Hanno Gottschalk; Matthias Rottmann (VISIGRAPP (2): VISAPP, 2024)

Have We Ever Encountered This Before? Retrieving Out-of-Distribution Road Obstacles from Driving Scenes. - Youssef Shoeb; Robin Chan; Gesina Schwalbe; Azarm Nowzad; Fatma Güney; Hanno Gottschalk (WACV, 2024)

AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving. - Daniel Bogdoll; Iramm Hamdard; Lukas Namgyu Rößler; Felix Geisler; Muhammed Bayram; Felix Wang; Jan Imhof; Miguel de Campos; Anushervon Tabarov; Yitian Yang; Hanno Gottschalk; J. Marius Zöllner (CoRR, 2024)

VL4AD: Vision-Language Models Improve Pixel-wise Anomaly Detection. - Liangyu Zhong; Joachim Sicking; Fabian Hüger; Hanno Gottschalk (CoRR, 2024)

On the Influence of Shape, Texture and Color for Learning Semantic Segmentation. - Annika Mütze; Natalie Grabowsky; Edgar Heinert; Matthias Rottmann; Hanno Gottschalk (CoRR, 2024)

New advances in universal approximation with neural networks of minimal width. - Dennis Rochau; Robin Chan; Hanno Gottschalk (CoRR, 2024)

Comparison of Generative Learning Methods for Turbulence Modeling. - Claudia Drygala; Edmund Ross; Francesca di Mare; Hanno Gottschalk (CoRR, 2024)

Out-of-Distribution Segmentation in Autonomous Driving: Problems and State of the Art. - Youssef Shoeb; Azarm Nowzad; Hanno Gottschalk (CVPR Workshops, 2025)

MetaToken: Detecting Hallucination in Image Descriptions by Meta Classification. - Laura Fieback; Jakob Spiegelberg; Hanno Gottschalk (VISIGRAPP (2): VISAPP, 2025)

Segment-Level Road Obstacle Detection Using Visual Foundation Model Priors and Likelihood Ratios. - Youssef Shoeb; Nazir Nayal; Azarm Nowzad; Fatma Güney; Hanno Gottschalk (VISIGRAPP (2): VISAPP, 2025)

Adaptive Neural Networks for Intelligent Data-Driven Development. - Youssef Shoeb; Azarm Nowzad; Hanno Gottschalk (CoRR, 2025)

Numerical and statistical analysis of NeuralODE with Runge-Kutta time integration. - Emily C. Ehrhardt; Hanno Gottschalk; Tobias Riedlinger (CoRR, 2025)

Efficient Contrastive Decoding with Probabilistic Hallucination Detection - Mitigating Hallucinations in Large Vision Language Models -. - Laura Fieback; Nishilkumar Balar; Jakob Spiegelberg; Hanno Gottschalk (CoRR, 2025)

Learning Brenier Potentials with Convex Generative Adversarial Neural Networks. - Claudia Drygala; Hanno Gottschalk; Thomas Kruse; Ségolène Martin; Annika Mütze (CoRR, 2025)

Generative AI for Autonomous Driving: A Review. - Katharina Winter; Abhishek Vivekanandan; Rupert Polley; Yinzhe Shen; Christian Schlauch; Mohamed-Khalil Bouzidi; Bojan Derajic; Natalie Grabowsky; Annajoyce Mariani; Dennis Rochau; Giovanni Lucente; Harsh Yadav; et al. (CoRR, 2025)

Robust Evolutionary Multi-Objective Network Architecture Search for Reinforcement Learning (EMNAS-RL). - Nihal Acharya Adde; Alexandra Gianzina; Hanno Gottschalk; Andreas Ebert (CoRR, 2025)

Learning to Integrate. - Oliver G. Ernst; Hanno Gottschalk; Toni Kowalewitz; Patrick Krüger (CoRR, 2025)

VideoGAN-based Trajectory Proposal for Automated Vehicles. - Annajoyce Mariani; Kira Maag; Hanno Gottschalk (CoRR, 2025)

Towards Reliable Detection of Empty Space: Conditional Marked Point Processes for Object Detection. - Tobias Riedlinger; Kira Maag; Hanno Gottschalk (CoRR, 2025)

FOCUS: Internal MLLM Representations for Efficient Fine-Grained Visual Question Answering. - Liangyu Zhong; Fabio Rosenthal; Joachim Sicking; Fabian Hüger; Thorsten Bagdonat; Hanno Gottschalk; Leo Schwinn (CoRR, 2025)

Consistency of Learned Sparse Grid Quadrature Rules using NeuralODEs. - Hanno Gottschalk; Emil Partow; Tobias Riedlinger (CoRR, 2025)

When do World Models Successfully Learn Dynamical Systems? - Edmund Ross; Claudia Drygala; Leonhard Schwarz; Samir Kaiser; Francesca di Mare; Tobias Breiten; Hanno Gottschalk (CoRR, 2025)