| Test-time adaptation to distribution shift by confidence maximization and input transformation CK Mummadi, R Hutmacher, K Rambach, E Levinkov, T Brox, JH Metzen arXiv preprint arXiv:2106.14999, 2021 | 50 | 2021 |
| Does enhanced shape bias improve neural network robustness to common corruptions? CK Mummadi, R Subramaniam, R Hutmacher, J Vitay, V Fischer, ... arXiv preprint arXiv:2104.09789, 2021 | 32 | 2021 |
| Calibrating uncertainty models for steering angle estimation C Hubschneider, R Hutmacher, JM Zöllner 2019 IEEE intelligent transportation systems conference (ITSC), 1511-1518, 2019 | 25 | 2019 |
| StreamPipes: solving the challenge with semantic stream processing pipelines D Riemer, F Kaulfersch, R Hutmacher, L Stojanovic Proceedings of the 9th ACM international conference on distributed event …, 2015 | 18 | 2015 |
| Meta adversarial training against universal patches JH Metzen, N Finnie, R Hutmacher arXiv preprint arXiv:2101.11453, 2021 | 14 | 2021 |
| Identification of systematic errors of image classifiers on rare subgroups JH Metzen, R Hutmacher, NG Hua, V Boreiko, D Zhang Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 9 | 2023 |
| Meta Adversarial Training JH Metzen, N Finnie, R Hutmacher | 6 | 2020 |
| Scene recognition for mobile robots by relational object search using next-best-view estimates from hierarchical implicit shape models P Meißner, R Schleicher, R Hutmacher, SR Schmidt-Rohr, R Dillmann 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2016 | 4 | 2016 |
| Anomaly-Aware Semantic Segmentation via Style-Aligned OoD Augmentation D Zhang, K Sakmann, W Beluch, R Hutmacher, Y Li Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 1 | 2023 |
| Device and method for training a classifier and assessing the robustness of a classifier R Hutmacher, JH Metzen, NY Finnie US Patent App. 17/228,126, 2021 | 1 | 2021 |
| Measuring the sensitivity of neural network image classifiers against adversarial attacks R Hutmacher, JH Metzen, NY Finnie US Patent App. 17/187,255, 2021 | 1 | 2021 |
| Data-based updating of the training of classifier networks CK Mummadi, JH Metzen, K Rambach, R Hutmacher US Patent 11,947,625, 2024 | | 2024 |
| Device and method for determining adversarial perturbations of a machine learning system NY Finnie, JH Metzen, R Hutmacher US Patent App. 18/331,044, 2023 | | 2023 |
| Data augmentation for domain generalization L Beggel, FJC CONDESSA, R Hutmacher, J KOLTER, NTP Ngo, ... US Patent App. 17/716,590, 2023 | | 2023 |
| Device and method for determining a semantic segmentation and/or an instance segmentation of an image CK Mummadi, JH Metzen, R Hutmacher US Patent App. 17/894,358, 2023 | | 2023 |
| Identification of Systematic Errors of Image Classifiers on Rare Subgroups J Hendrik Metzen, R Hutmacher, NG Hua, V Boreiko, D Zhang arXiv e-prints, arXiv: 2303.05072, 2023 | | 2023 |
| Device and method to adapt a pretrained machine learning system to target data that has different distribution than the training data without the necessity of human annotations … CK Mummadi, E Levinkov, JH Metzen, K Rambach, R Hutmacher US Patent App. 17/747,361, 2022 | | 2022 |
| Device and method for training a classifier R Hutmacher, JH Metzen, NY Finnie US Patent App. 17/225,484, 2021 | | 2021 |
| Meta Adversarial Training against Universal Patches J Hendrik Metzen, N Finnie, R Hutmacher arXiv e-prints, arXiv: 2101.11453, 2021 | | 2021 |
