| Challenges of reliable, realistic and comparable active learning evaluation D Kottke, A Calma, D Huseljic, GM Krempl, B Sick Proceedings of the Workshop and Tutorial on Interactive Adaptive Learning, 2-14, 2017 | 38 | 2017 |
| Out-of-distribution detection and generation using soft brownian offset sampling and autoencoders F Moller, D Botache, D Huseljic, F Heidecker, M Bieshaar, B Sick Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 28 | 2021 |
| Toward optimal probabilistic active learning using a Bayesian approach D Kottke, M Herde, C Sandrock, D Huseljic, G Krempl, B Sick Machine Learning 110 (6), 1199-1231, 2021 | 24 | 2021 |
| Separation of aleatoric and epistemic uncertainty in deterministic deep neural networks D Huseljic, B Sick, M Herde, D Kottke 2020 25th International Conference on Pattern Recognition (ICPR), 9172-9179, 2021 | 24 | 2021 |
| A survey on cost types, interaction schemes, and annotator performance models in selection algorithms for active learning in classification M Herde, D Huseljic, B Sick, A Calma IEEE Access 9, 166970-166989, 2021 | 23 | 2021 |
| Limitations of assessing active learning performance at runtime D Kottke, J Schellinger, D Huseljic, B Sick arXiv preprint arXiv:1901.10338, 2019 | 14 | 2019 |
| Multi-annotator probabilistic active learning M Herde, D Kottke, D Huseljic, B Sick 2020 25th International Conference on Pattern Recognition (ICPR), 10281-10288, 2021 | 6 | 2021 |
| The other human in the loop–A pilot study to find selection strategies for active learning D Kottke, A Calma, D Huseljic, C Sandrock, G Kachergis, B Sick 2018 International Joint Conference on Neural Networks (IJCNN), 1-8, 2018 | 4 | 2018 |
| Towards proactive health-enabling living environments: Simulation-based study and research challenges S Tomforde, T Dehling, R Haux, D Huseljic, D Kottke, J Scheerbaum, ... ARCS Workshop 2018; 31th International Conference on Architecture of …, 2018 | 4 | 2018 |
| Enhancing Active Learning with Weak Supervision and Transfer Learning by Leveraging Information and Knowledge Sources. L Rauch, D Huseljic, B Sick IAL@ PKDD/ECML, 27-42, 2022 | 3 | 2022 |
| ActiveGLAE: A Benchmark for Deep Active Learning with Transformers L Rauch, M Aßenmacher, D Huseljic, M Wirth, B Bischl, B Sick Joint European Conference on Machine Learning and Knowledge Discovery in …, 2023 | 2 | 2023 |
| Who knows best? A Case Study on Intelligent Crowdworker Selection via Deep Learning M Herde, D Huseljic, B Sick, U Bretschneider, S Oeste-Reiß Int. Workshop on Interact. Adapt. Learn.@ Eur. Conf. Mach. Learn, 14-18, 2023 | 1 | 2023 |
| Multi-annotator Deep Learning: A Probabilistic Framework for Classification M Herde, D Huseljic, B Sick arXiv preprint arXiv:2304.02539, 2023 | 1 | 2023 |
| Fast Bayesian Updates for Deep Learning with a Use Case in Active Learning M Herde, Z Huang, D Huseljic, D Kottke, S Vogt, B Sick arXiv preprint arXiv:2210.06112, 2022 | 1 | 2022 |
| Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension M Herde, L Lührs, D Huseljic, B Sick arXiv preprint arXiv:2405.03386, 2024 | | 2024 |
| Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image Classification D Huseljic, P Hahn, M Herde, L Rauch, B Sick arXiv preprint arXiv:2404.08981, 2024 | | 2024 |
| Active Label Refinement for Semantic Segmentation of Satellite Images TP Minh, J Wijesingha, D Kottke, M Herde, D Huseljic, B Sick, ... arXiv preprint arXiv:2309.06159, 2023 | | 2023 |
| Active Label Refinement for Semantic Segmentation of Satellite Images T Pham Minh, J Wijesingha, D Kottke, M Herde, D Huseljic, B Sick, ... arXiv e-prints, arXiv: 2309.06159, 2023 | | 2023 |
| Role of Hyperparameters in Deep Active Learning D Huseljic, M Herde, P Hahn, B Sick | | 2023 |
| Active Learning with Fast Model Updates and Class-Balanced Selection for Imbalanced Datasets Z Huang, Y He, M Herde, D Huseljic, B Sick | | 2023 |
