| A call to reflect on evaluation practices for failure detection in image classification PF Jaeger, CT Lüth, L Klein, TJ Bungert arXiv preprint arXiv:2211.15259, 2022 | 50 | 2022 |
| Navigating the pitfalls of active learning evaluation: A systematic framework for meaningful performance assessment C Lüth, T Bungert, L Klein, P Jaeger Advances in Neural Information Processing Systems 36, 9789-9836, 2023 | 11 | 2023 |
| Understanding silent failures in medical image classification TJ Bungert, L Kobelke, PF Jaeger International Conference on Medical Image Computing and Computer-Assisted …, 2023 | 7 | 2023 |
| Toward realistic evaluation of deep active learning algorithms in image classification CT Lüth, TJ Bungert, L Klein, PF Jaeger arXiv preprint arXiv:2301.10625 2, 2023 | 7 | 2023 |
| Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metrics L Klein, CT Lüth, U Schlegel, TJ Bungert, M El-Assady, PF Jäger arXiv preprint arXiv:2409.16756, 2024 | 3 | 2024 |
| Overcoming common flaws in the evaluation of selective classification systems J Traub, TJ Bungert, CT Lüth, M Baumgartner, KH Maier-Hein, ... arXiv preprint arXiv:2407.01032, 2024 | 1 | 2024 |
| LATEC—A benchmark for large-scale attribution & attention evaluation in computer vision L Klein, U Schlegel, TJ Bungert, CT Lüth, M El-Assady, PF Jaeger | | |
