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Hanno Gottschalk
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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
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
Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving. IEEE Access 11: 54296-54336 (2023) - [j10]Robin Chan
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
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
Detection of Iterative Adversarial Attacks via Counter Attack. J. Optim. Theory Appl. 198(3): 892-929 (2023) - [c23]Laura Fieback
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
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
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
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
Spontaneous Wave Formation in Stochastic Self-Driven Particle Systems. SIAM J. Appl. Math. 81(3): 853-870 (2021) - [j5]Matthias Bolten
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
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
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
Detection of False Positive and False Negative Samples in Semantic Segmentation. DATE 2020: 1351-1356 - [c6]Kira Maag
Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation Networks. ICTAI 2020: 502-509 - [c5]Robin Chan
Controlled False Negative Reduction of Minority Classes in Semantic Segmentation. IJCNN 2020: 1-8 - [c4]Matthias Rottmann, Pascal Colling, Thomas-Paul Hack, Robin Chan
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
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
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
Numerical shape optimization to decrease failure probability of ceramic structures. Comput. Vis. Sci. 21(1-6): 1-10 (2019) - [c3]Robin Chan
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
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
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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last updated on 2024-10-18 19:29 CEST by the dblp team
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