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2020 – today
- 2024
[c26]Julian Burghoff, Matthias Rottmann, Jill von Conta, Sebastian Schoenen, Andreas Witte, Hanno Gottschalk:
ResBuilder: Automated Learning of Depth with Residual Structures. ICANN (1) 2024: 308-323- [c25]Tobias Riedlinger, Marius Schubert, Karsten Kahl, Hanno Gottschalk, Matthias Rottmann:
Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection. VISIGRAPP (2): VISAPP 2024: 366-374 - [c24]Youssef Shoeb, R. Chan, Gesina Schwalbe
, Azarm Nowzad, Fatma Güney, Hanno Gottschalk:
Have We Ever Encountered This Before? Retrieving Out-of-Distribution Road Obstacles from Driving Scenes. WACV 2024: 7381-7391 - [i50]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:
AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving. CoRR abs/2405.07865 (2024) - [i49]Laura Fieback, Jakob Spiegelberg, Hanno Gottschalk:
MetaToken: Detecting Hallucination in Image Descriptions by Meta Classification. CoRR abs/2405.19186 (2024) - [i48]Nihal Acharya Adde, Hanno Gottschalk, Andreas Ebert:
Hyperparameter Optimization for Driving Strategies Based on Reinforcement Learning. CoRR abs/2407.14262 (2024) - [i47]Liangyu Zhong, Joachim Sicking, Fabian Hüger, Hanno Gottschalk:
VL4AD: Vision-Language Models Improve Pixel-wise Anomaly Detection. CoRR abs/2409.17330 (2024) - 2023
- [j11]Manuel Schwonberg, Joshua Niemeijer, Jan-Aike Termöhlen, Jörg P. Schäfer, Nico M. Schmidt, Hanno Gottschalk, Tim Fingscheidt:
Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving. IEEE Access 11: 54296-54336 (2023) - [j10]Robin Chan, Radin Dardashti, Meike Osinski, Matthias Rottmann, Dominik Brüggemann, Cilia Rücker, Peter Schlicht, Fabian Hüger, Nikol Rummel, Hanno Gottschalk:
What should AI see? Using the public's opinion to determine the perception of an AI. AI Ethics 3(4): 1381-1405 (2023) - [j9]Pascal Colling, Matthias Rottmann, Lutz Roese-Koerner, Hanno Gottschalk:
Prediction Quality Meta Regression and Error Meta Classification for Segmented Lidar Point Clouds. Int. J. Artif. Intell. Tools 32(5): 2360006:1-2360006:25 (2023) - [j8]Matthias Rottmann, Kira Maag, Mathis Peyron, Hanno Gottschalk, Natasa Krejic:
Detection of Iterative Adversarial Attacks via Counter Attack. J. Optim. Theory Appl. 198(3): 892-929 (2023) - [c23]Laura Fieback, Bidya Dash, Jakob Spiegelberg, Hanno Gottschalk:
Temporal Performance Prediction for Deep Convolutional Long Short-Term Memory Networks. AALTD@ECML/PKDD 2023: 145-158 - [c22]Julian Burghoff, Leonhard Ackermann, Younes Salahdine, Veronika Bram, Katharina Wunderlich, Julius Balkenhol, Thomas Dirschka, Hanno Gottschalk:
Risk Stratification of Malignant Melanoma Using Neural Networks. ICANN (4) 2023: 153-164 - [c21]Julian Burghoff, Marc Heinrich Monells, Hanno Gottschalk:
Who Breaks Early, Looses: Goal Oriented Training of Deep Neural Networks Based on Port Hamiltonian Dynamics. ICANN (10) 2023: 454-465 - [c20]Robin Chan, Sarina Penquitt, Hanno Gottschalk:
LU-Net: Invertible Neural Networks Based on Matrix Factorization. IJCNN 2023: 1-10 - [c19]Manuel Schwonberg, Fadoua El Bouazati, Nico M. Schmidt, Hanno Gottschalk:
Augmentation-based Domain Generalization for Semantic Segmentation. IV 2023: 1-8 - [c18]Annika Mütze, Matthias Rottmann, Hanno Gottschalk:
Semi-Supervised Domain Adaptation with CycleGAN Guided by Downstream Task Awareness. VISIGRAPP (5: VISAPP) 2023: 80-90 - [c17]Tobias Riedlinger, Matthias Rottmann, Marius Schubert, Hanno Gottschalk:
Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors. WACV 2023: 3910-3920 - [i46]Patrick Krüger, Hanno Gottschalk:
Equivariant and Steerable Neural Networks: A review with special emphasis on the symmetric group. CoRR abs/2301.03019 (2023) - [i45]Hayk Asatryan, Daniela Gaul, Hanno Gottschalk, Kathrin Klamroth, Michael Stiglmayr:
Ridepooling and public bus services: A comparative case-study. CoRR abs/2302.01709 (2023) - [i44]Claudia Drygala, Francesca di Mare, Hanno Gottschalk:
Generalization capabilities of conditional GAN for turbulent flow under changes of geometry. CoRR abs/2302.09945 (2023) - [i43]Robin Chan, Sarina Penquitt, Hanno Gottschalk:
LU-Net: Invertible Neural Networks Based on Matrix Factorization. CoRR abs/2302.10524 (2023) - [i42]Matthias Bolten, Onur Tanil Doganay, Hanno Gottschalk, Kathrin Klamroth:
Non-convex shape optimization by dissipative Hamiltonian flows. CoRR abs/2303.01369 (2023) - [i41]Julian Burghoff, Marc Heinrich Monells, Hanno Gottschalk:
Who breaks early, looses: goal oriented training of deep neural networks based on port Hamiltonian dynamics. CoRR abs/2304.07070 (2023) - [i40]Manuel Schwonberg, Joshua Niemeijer, Jan-Aike Termöhlen, Jörg P. Schäfer, Nico M. Schmidt, Hanno Gottschalk, Tim Fingscheidt:
Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving. CoRR abs/2304.11928 (2023) - [i39]Manuel Schwonberg, Fadoua El Bouazati, Nico M. Schmidt, Hanno Gottschalk:
Augmentation-based Domain Generalization for Semantic Segmentation. CoRR abs/2304.12122 (2023) - [i38]Svenja Uhlemeyer, Julian Lienen, Eyke Hüllermeier, Hanno Gottschalk:
Detecting Novelties with Empty Classes. CoRR abs/2305.00983 (2023) - [i37]Kamil Kowol, Stefan Bracke, Hanno Gottschalk:
survAIval: Survival Analysis with the Eyes of AI. CoRR abs/2305.18222 (2023) - [i36]Julian Burghoff, Leonhard Ackermann, Younes Salahdine, Veronika Bram, Katharina Wunderlich, Julius Balkenhol, Thomas Dirschka, Hanno Gottschalk:
Risk stratification of malignant melanoma using neural networks. CoRR abs/2306.06195 (2023) - [i35]Julian Burghoff, Matthias Rottmann, Jill von Conta, Sebastian Schoenen, Andreas Witte, Hanno Gottschalk:
ResBuilder: Automated Learning of Depth with Residual Structures. CoRR abs/2308.08504 (2023) - [i34]Youssef Shoeb, Robin Chan, Gesina Schwalbe, Azarm Nowzad, Fatma Güney, Hanno Gottschalk:
Have We Ever Encountered This Before? Retrieving Out-of-Distribution Road Obstacles from Driving Scenes. CoRR abs/2309.04302 (2023) - [i33]Laura Fieback, Bidya Dash, Jakob Spiegelberg, Hanno Gottschalk:
Temporal Performance Prediction for Deep Convolutional Long Short-Term Memory Networks. CoRR abs/2311.07477 (2023) - [i32]Christoph Hümmer, Manuel Schwonberg, Liangwei Zhou, Hu Cao, Alois Knoll, Hanno Gottschalk:
VLTSeg: Simple Transfer of CLIP-Based Vision-Language Representations for Domain Generalized Semantic Segmentation. CoRR abs/2312.02021 (2023) - 2022
- [c16]Kira Maag, Robin Chan, Svenja Uhlemeyer, Kamil Kowol, Hanno Gottschalk:
Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects. ACCV (5) 2022: 476-494 - [c15]Kamil Kowol, Stefan Bracke, Hanno Gottschalk:
A-Eye: Driving with the Eyes of AI for Corner Case Generation. CHIRA 2022: 41-48 - [c14]Kamil Kowol, Stefan Bracke, Hanno Gottschalk:
survAIval: Survival Analysis with the Eyes of AI. CHIRA (Revised Selected Papers) 2022: 153-170 - [c13]Svenja Uhlemeyer, Matthias Rottmann, Hanno Gottschalk:
Towards unsupervised open world semantic segmentation. UAI 2022: 1981-1991 - [i31]Svenja Uhlemeyer, Matthias Rottmann, Hanno Gottschalk:
Towards Unsupervised Open World Semantic Segmentation. CoRR abs/2201.01073 (2022) - [i30]Robin Chan, Svenja Uhlemeyer, Matthias Rottmann, Hanno Gottschalk:
Detecting and Learning the Unknown in Semantic Segmentation. CoRR abs/2202.08700 (2022) - [i29]Kamil Kowol, Stefan Bracke, Hanno Gottschalk:
A-Eye: Driving with the Eyes of AI for Corner Case Generation. CoRR abs/2202.10803 (2022) - [i28]Julian Burghoff, Robin Chan, Hanno Gottschalk, Annika Mütze, Tobias Riedlinger, Matthias Rottmann, Marius Schubert:
Uncertainty Quantification and Resource-Demanding Computer Vision Applications of Deep Learning. CoRR abs/2205.14917 (2022) - [i27]Robin Chan, Radin Dardashti, Meike Osinski, Matthias Rottmann, Dominik Brüggemann, Cilia Rücker, Peter Schlicht, Fabian Hüger, Nikol Rummel, Hanno Gottschalk:
What should AI see? Using the Public's Opinion to Determine the Perception of an AI. CoRR abs/2206.04776 (2022) - [i26]Annika Mütze, Matthias Rottmann, Hanno Gottschalk:
Semi-supervised domain adaptation with CycleGAN guided by a downstream task loss. CoRR abs/2208.08815 (2022) - [i25]Kira Maag, Robin Chan, Svenja Uhlemeyer, Kamil Kowol, Hanno Gottschalk:
Two Video Data Sets for Tracking and Retrieval of Out of Distribution Objects. CoRR abs/2210.02074 (2022) - [i24]Tobias Riedlinger, Marius Schubert, Karsten Kahl, Hanno Gottschalk, Matthias Rottmann:
Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection. CoRR abs/2212.10836 (2022) - 2021
- [j7]Hanno Gottschalk, Marco Reese:
An Analytical Study in Multi-physics and Multi-criteria Shape Optimization. J. Optim. Theory Appl. 189(2): 486-512 (2021) - [j6]Martin Friesen, Hanno Gottschalk, Barbara Rüdiger, Antoine Tordeux:
Spontaneous Wave Formation in Stochastic Self-Driven Particle Systems. SIAM J. Appl. Math. 81(3): 853-870 (2021) - [j5]Matthias Bolten, Onur Tanil Doganay, Hanno Gottschalk, Kathrin Klamroth:
Tracing Locally Pareto-Optimal Points by Numerical Integration. SIAM J. Control. Optim. 59(5): 3302-3328 (2021) - [c12]Kamil Kowol, Matthias Rottmann, Stefan Bracke, Hanno Gottschalk:
YOdar: Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors. ICAART (2) 2021: 177-186 - [c11]Robin Chan, Matthias Rottmann, Hanno Gottschalk:
Entropy Maximization and Meta Classification for Out-of-Distribution Detection in Semantic Segmentation. ICCV 2021: 5108-5117 - [c10]Pascal Colling, Lutz Roese-Koerner, Hanno Gottschalk, Matthias Rottmann:
MetaBox+: A New Region based Active Learning Method for Semantic Segmentation using Priority Maps. ICPRAM 2021: 51-62 - [c9]Pascal Colling, Matthias Rottmann, Lutz Roese-Koerner, Hanno Gottschalk:
False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation. ICTAI 2021: 18-25 - [c8]Kira Maag, Matthias Rottmann, Serin Varghese, Fabian Hüger, Peter Schlicht, Hanno Gottschalk:
Improving Video Instance Segmentation by Light-weight Temporal Uncertainty Estimates. IJCNN 2021: 1-8 - [i23]Tobias Riedlinger, Matthias Rottmann, Marius Schubert, Hanno Gottschalk:
Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors. CoRR abs/2107.04517 (2021) - [i22]Claudia Drygala, Matthias Rottmann, Hanno Gottschalk, Klaus Friedrichs, Thomas Kurbiel:
Background-Foreground Segmentation for Interior Sensing in Automotive Industry. CoRR abs/2109.09410 (2021) - [i21]Pascal Colling, Matthias Rottmann, Lutz Roese-Koerner, Hanno Gottschalk:
False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation. CoRR abs/2110.15681 (2021) - [i20]Claudia Drygala, Benjamin Winhart, Francesca di Mare, Hanno Gottschalk:
Generative Modeling of Turbulence. CoRR abs/2112.02548 (2021) - [i19]Hanno Gottschalk, Matthias Rottmann, Maida Saltagic:
Does Redundancy in AI Perception Systems Help to Test for Super-Human Automated Driving Performance? CoRR abs/2112.04758 (2021) - 2020
- [c7]Matthias Rottmann, Kira Maag, Robin Chan, Fabian Hüger, Peter Schlicht, Hanno Gottschalk:
Detection of False Positive and False Negative Samples in Semantic Segmentation. DATE 2020: 1351-1356 - [c6]Kira Maag, Matthias Rottmann, Hanno Gottschalk:
Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks. ICTAI 2020: 502-509 - [c5]Robin Chan, Matthias Rottmann, Fabian Hüger, Peter Schlicht, Hanno Gottschalk:
Controlled False Negative Reduction of Minority Classes in Semantic Segmentation. IJCNN 2020: 1-8 - [c4]Matthias Rottmann, Pascal Colling, Thomas-Paul Hack, Robin Chan, Fabian Hüger, Peter Schlicht, Hanno Gottschalk:
Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities. IJCNN 2020: 1-9 - [i18]Jan Backhaus, Matthias Bolten, Onur Tanil Doganay, Matthias Ehrhardt, Benedikt Engel, Christian Frey, Hanno Gottschalk, Michael Günther, Camilla Hahn, Jens Jäschke, Peter Jaksch, Kathrin Klamroth, Alexander Liefke, Daniel Luft, Lucas Mäde, Vincent Marciniak, Marco Reese, Johanna Schultes, Volker Schulz, Sebastian Schmitz, Johannes Steiner, Michael Stiglmayr:
GivEn - Shape Optimization for Gas Turbines in Volatile Energy Networks. CoRR abs/2002.08672 (2020) - [i17]Matthias Bolten, Onur Tanil Doganay, Hanno Gottschalk, Kathrin Klamroth:
Tracing locally Pareto optimal points by numerical integration. CoRR abs/2004.10820 (2020) - [i16]Hanno Gottschalk, Karsten Kahl:
Coarsening in Algebraic Multigrid using Gaussian Processes. CoRR abs/2004.11427 (2020) - [i15]Matthias Rottmann, Mathis Peyron, Natasa Krejic, Hanno Gottschalk:
Detection of Iterative Adversarial Attacks via Counter Attack. CoRR abs/2009.11397 (2020) - [i14]Pascal Colling, Lutz Roese-Koerner, Hanno Gottschalk, Matthias Rottmann:
MetaBox+: A new Region Based Active Learning Method for Semantic Segmentation using Priority Maps. CoRR abs/2010.01884 (2020) - [i13]Kamil Kowol, Matthias Rottmann, Stefan Bracke, Hanno Gottschalk:
YOdar: Uncertainty-based Sensor Fusion for Vehicle Detection with Camera and Radar Sensors. CoRR abs/2010.03320 (2020) - [i12]Hayk Asatryan, Hanno Gottschalk, Marieke Lippert, Matthias Rottmann:
A Convenient Infinite Dimensional Framework for Generative Adversarial Learning. CoRR abs/2011.12087 (2020) - [i11]Robin Chan, Matthias Rottmann, Hanno Gottschalk:
Entropy Maximization and Meta Classification for Out-Of-Distribution Detection in Semantic Segmentation. CoRR abs/2012.06575 (2020) - [i10]Kira Maag, Matthias Rottmann, Fabian Hüger, Peter Schlicht, Hanno Gottschalk:
Improving Video Instance Segmentation by Light-weight Temporal Uncertainty Estimates. CoRR abs/2012.07504 (2020)
2010 – 2019
- 2019
- [j4]Matthias Bolten, Hanno Gottschalk, Camilla Hahn, Mohamed Saadi:
Numerical shape optimization to decrease failure probability of ceramic structures. Comput. Vis. Sci. 21(1-6): 1-10 (2019) - [c3]Robin Chan, Matthias Rottmann, Radin Dardashti, Fabian Hüger, Peter Schlicht, Hanno Gottschalk:
The Ethical Dilemma When (Not) Setting up Cost-Based Decision Rules in Semantic Segmentation. CVPR Workshops 2019: 1395-1403 - [i9]Robin Chan, Matthias Rottmann, Fabian Hüger, Peter Schlicht, Hanno Gottschalk:
Application of Decision Rules for Handling Class Imbalance in Semantic Segmentation. CoRR abs/1901.08394 (2019) - [i8]Alexander Liefke, Vincent Marciniak, Uwe Janoske, Hanno Gottschalk:
Using adjoint CFD to quantify the impact of manufacturing variations on a heavy duty turbine vane. CoRR abs/1901.10352 (2019) - [i7]Robin Chan, Matthias Rottmann, Radin Dardashti, Fabian Hüger, Peter Schlicht, Hanno Gottschalk:
The Ethical Dilemma when (not) Setting up Cost-based Decision Rules in Semantic Segmentation. CoRR abs/1907.01342 (2019) - [i6]Kira Maag, Matthias Rottmann, Hanno Gottschalk:
Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks. CoRR abs/1911.05075 (2019) - [i5]Matthias Rottmann, Kira Maag, Robin Chan, Fabian Hüger, Peter Schlicht, Hanno Gottschalk:
Detection of False Positive and False Negative Samples in Semantic Segmentation. CoRR abs/1912.03673 (2019) - [i4]Robin Chan, Matthias Rottmann, Fabian Hüger, Peter Schlicht, Hanno Gottschalk:
MetaFusion: Controlled False-Negative Reduction of Minority Classes in Semantic Segmentation. CoRR abs/1912.07420 (2019) - 2018
- [j3]Mario Annunziato, Hanno Gottschalk:
Calibration of léVY Processes using Optimal control of Kolmogorov equations with periodic boundary conditions. Math. Model. Anal. 23(3): 390-413 (2018) - [c2]Philipp Oberdiek, Matthias Rottmann, Hanno Gottschalk:
Classification Uncertainty of Deep Neural Networks Based on Gradient Information. ANNPR 2018: 113-125 - [c1]Matthias Rottmann, Karsten Kahl, Hanno Gottschalk:
Deep Bayesian Active Semi-Supervised Learning. ICMLA 2018: 158-164 - [i3]Matthias Rottmann, Karsten Kahl, Hanno Gottschalk:
Deep Bayesian Active Semi-Supervised Learning. CoRR abs/1803.01216 (2018) - [i2]Philipp Oberdiek, Matthias Rottmann, Hanno Gottschalk:
Classification Uncertainty of Deep Neural Networks Based on Gradient Information. CoRR abs/1805.08440 (2018) - [i1]Matthias Rottmann, Pascal Colling, Thomas-Paul Hack, Fabian Hüger, Peter Schlicht, Hanno Gottschalk:
Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities. CoRR abs/1811.00648 (2018) - 2015
- [j2]Matthias Bolten, Hanno Gottschalk, Sebastian Schmitz:
Minimal Failure Probability for Ceramic Design Via Shape Control. J. Optim. Theory Appl. 166(3): 983-1001 (2015) - 2014
- [j1]Hanno Gottschalk, Sebastian Schmitz:
Optimal Reliability in Design for Fatigue Life. SIAM J. Control. Optim. 52(5): 2727-2752 (2014)
Coauthor Index
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