| Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges D Feng, C Haase-Schuetz, L Rosenbaum, H Hertlein, F Duffhauss, ... arXiv:1902.07830, 2019 | 976 | 2019 |
| Adaptive stochastic resonance for unknown and variable input signals P Krauss, C Metzner, A Schilling, C Schütz, K Tziridis, B Fabry, H Schulze Scientific reports 7 (1), 1-8, 2017 | 73 | 2017 |
| Estimating Labeling Quality with Deep Object Detectors C Haase-Schütz, H Hertlein, W Wiesbeck 2019 IEEE Intelligent Vehicles Symposium (IV), pp. 33-38., 2019 | 10 | 2019 |
| Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning C Haase-Schütz, R Stal, H Hertlein, B Sick 2020 25th International Conference on Pattern Recognition (ICPR), 9483-9490, 2021 | 9 | 2021 |
| Device and computer-implemented method for data-efficient active machine learning C Haase-Schuetz, P Moeller US Patent App. 17/142,941, 2021 | 4 | 2021 |
| Method for Generating Training Data for a Recognition Model for Recognizing Objects in Sensor Data from a Surroundings Sensor System of a Vehicle, Method for Generating a … C Haase-Schuetz, H Hertlein, J Liedtke US Patent App. 17/529,737, 2022 | 1 | 2022 |
| Method for determining a quality grade of data sets of sensors R Stal, C Haase-Schuetz, H Hertlein US Patent App. 17/063,388, 2021 | 1 | 2021 |
| Method for Determining Training Data for Training a Model, in particular for Solving a Recognition Task C Haase-Schuetz, H Hertlein, J Liedtke, O Rogalla US Patent App. 18/461,577, 2024 | | 2024 |
| Deep Learning Based Multi-modal Perception and Semi-automatic Labelling Algorithms for Automotive Sensor Data C Haase-Schütz kassel university press, 2023 | | 2023 |
| Robust artificial neural network having improved trainability C Haase-Schuetz, F Schmidt, T Sachse US Patent App. 17/637,890, 2022 | | 2022 |
| Method for generating labeled data, in particular for training a neural network, by improving initial labels A Feyerabend, A Blonczewski, C Haase-Schuetz, E Pancera, H Hertlein, ... US Patent App. 17/129,393, 2021 | | 2021 |
| Device and computer-implemented method for data-efficient active machine learning C Haase-Schuetz US Patent App. 17/142,930, 2021 | | 2021 |
| Method for generating labeled data, in particular for training a neural network, by using unlabeled partitioned samples C Haase-Schuetz, H Hertlein, R Stal US Patent App. 17/117,260, 2021 | | 2021 |
| Trust Your Model: Iterative Label Improvement and Robust Training by Confidence Based Filtering and Dataset Partitioning C Haase-Schütz, R Stal, H Hertlein, B Sick arXiv preprint arXiv:2002.02705, 2020 | | 2020 |
| Taking advantage of sensor modality specific properties in Automated Driving C Haase-Schuetz, H Hertlein | | 2018 |
Di FengSenior Research Scientist, Apple Inc.Bestätigte E-Mail-Adresse bei berkeley.edu
