A branch--and--bound-based algorithm for nonconvex multiobjective optimization J Niebling, G Eichfelder SIAM Journal on Optimization 29 (1), 794-821, 2019 | 53 | 2019 |
Solving multiobjective mixed integer convex optimization problems M De Santis, G Eichfelder, J Niebling, S Rocktäschel SIAM Journal on Optimization 30 (4), 3122-3145, 2020 | 50 | 2020 |
Evaluation of multi feature fusion at score-level for appearance-based person re-identification M Eisenbach, A Kolarow, A Vorndran, J Niebling, HM Gross 2015 international joint conference on neural networks (IJCNN), 1-8, 2015 | 32 | 2015 |
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series F Rewicki, J Denzler, J Niebling Applied Sciences 13 (3), 1778, 2023 | 21 | 2023 |
An algorithmic approach to multiobjective optimization with decision uncertainty G Eichfelder, J Niebling, S Rocktäschel Journal of Global Optimization 77 (1), 3-25, 2020 | 20 | 2020 |
Analysis of railway track irregularities with convolutional autoencoders and clustering algorithms J Niebling, B Baasch, A Kruspe European Dependable Computing Conference, 78-89, 2020 | 11 | 2020 |
Domain shifts in dermoscopic skin cancer datasets: Evaluation of essential limitations for clinical translation K Fogelberg, S Chamarthi, RC Maron, J Niebling, TJ Brinker New Biotechnology 76, 106-117, 2023 | 10 | 2023 |
Nonconvex constrained optimization by a filtering branch and bound G Eichfelder, K Klamroth, J Niebling Journal of Global Optimization 80 (1), 31-61, 2021 | 9 | 2021 |
Impact of training set size on the ability of deep neural networks to deal with omission noise J Gütter, A Kruspe, XX Zhu, J Niebling Frontiers in Remote Sensing 3, 932431, 2022 | 7 | 2022 |
Is it worth it? An experimental comparison of six deep-and classical machine learning methods for unsupervised anomaly detection in time series F Rewicki, J Denzler, J Niebling CoRR, 2022 | 6 | 2022 |
A branch-and-bound algorithm for biobjective problems J Niebling, G Eichfelder Proceedings of the XIII Global Optimization Workshop GOW16, 57-60, 2016 | 4 | 2016 |
Mitigating the Influence of Domain Shift in Skin Lesion Classification: A Benchmark Study of Unsupervised Domain Adaptation Methods on Dermoscopic Images S Chamarthi, K Fogelberg, RC Maron, TJ Brinker, J Niebling arXiv preprint arXiv:2310.03432, 2023 | 2 | 2023 |
Handling unexpected inputs: incorporating source-wise out-of-distribution detection into SAR-optical data fusion for scene classification J Gawlikowski, S Saha, J Niebling, XX Zhu EURASIP Journal on Advances in Signal Processing 2023 (1), 47, 2023 | 2 | 2023 |
Analysing the Interactions Between Training Dataset Size, Label Noise and Model Performance in Remote Sensing Data J Gütter, J Niebling, XX Zhu IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium …, 2022 | 2 | 2022 |
Using a B&B algorithm from multiobjective optimization to solve constrained optimization problems G Eichfelder, K Klamroth, J Niebling AIP Conference Proceedings 2070 (1), 2019 | 2 | 2019 |
Mitigating the influence of domain shift in skin lesion classification: A benchmark study of unsupervised domain adaptation methods S Chamarthi, K Fogelberg, TJ Brinker, J Niebling Informatics in Medicine Unlocked 44, 101430, 2024 | 1 | 2024 |
Uncertainty is not sufficient for identifying noisy labels in training data for binary segmentation of building footprints H Ulman, J Gütter, J Niebling Frontiers in Remote Sensing 3, 1100012, 2023 | 1 | 2023 |
Structuring uncertainty for fine-grained sampling in stochastic segmentation networks F Nussbaum, J Gawlikowski, J Niebling Advances in Neural Information Processing Systems 35, 27678-27691, 2022 | 1 | 2022 |
Robust distribution-shift aware sar-optical data fusion for multi-label scene classification J Gawlikowski, S Saha, J Niebling, XX Zhu IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium …, 2022 | 1 | 2022 |
Unsupervised Anomaly Detection for Space Gardening F Rewicki, J Denzler, J Niebling | | 2023 |