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Robin Hutmacher
Robin Hutmacher
Bosch Center for Artificial Intelligence
Verified email at de.bosch.com
Title
Cited by
Cited by
Year
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
472021
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
312021
Calibrating uncertainty models for steering angle estimation
C Hubschneider, R Hutmacher, JM Zöllner
2019 IEEE intelligent transportation systems conference (ITSC), 1511-1518, 2019
252019
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
182015
Meta adversarial training against universal patches
JH Metzen, N Finnie, R Hutmacher
arXiv preprint arXiv:2101.11453, 2021
132021
Meta Adversarial Training
JH Metzen, N Finnie, R Hutmacher
62020
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
52023
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
42016
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
12021
Measuring the sensitivity of neural network image classifiers against adversarial attacks
R Hutmacher, JH Metzen, NY Finnie
US Patent App. 17/187,255, 2021
12021
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
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
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
Data-based updating of the training of classifier networks
CK Mummadi, JH Metzen, K Rambach, R Hutmacher
US Patent App. 17/479,161, 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
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