| Mlcapsule: Guarded offline deployment of machine learning as a service L Hanzlik, Y Zhang, K Grosse, A Salem, M Augustin, M Backes, M Fritz Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 98 | 2021 |
| Adversarial robustness on in-and out-distribution improves explainability M Augustin, A Meinke, M Hein European Conference on Computer Vision, 228-245, 2020 | 71 | 2020 |
| Breaking down out-of-distribution detection: Many methods based on ood training data estimate a combination of the same core quantities J Bitterwolf, A Meinke, M Augustin, M Hein International Conference on Machine Learning, 2041-2074, 2022 | 20 | 2022 |
| Diffusion Visual Counterfactual Explanations M Augustin, V Boreiko, F Croce, M Hein NeurIPS 2022, 2022 | 19 | 2022 |
| Sparse visual counterfactual explanations in image space V Boreiko, M Augustin, F Croce, P Berens, M Hein DAGM German Conference on Pattern Recognition, 133-148, 2022 | 14 | 2022 |
| Spurious features everywhere-large-scale detection of harmful spurious features in imagenet Y Neuhaus, M Augustin, V Boreiko, M Hein Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 5 | 2023 |
| Out-distribution aware Self-training in an Open World Setting M Augustin, M Hein arXiv preprint arXiv:2012.12372, 2020 | 4 | 2020 |
| Whitening black-box neural networks SJ Oh, M Augustin, B Schiele, M Fritz arXiv, 2017 | 3 | 2017 |
| Revisiting out-of-distribution detection: A simple baseline is surprisingly effective J Bitterwolf, A Meinke, M Augustin, M Hein | 1 | 2021 |
| Analyzing and Explaining Image Classifiers via Diffusion Guidance M Augustin, Y Neuhaus, M Hein arXiv preprint arXiv:2311.17833, 2023 | | 2023 |
| Spurious Features Everywhere-Large-Scale Detection of Harmful Spurious Features in ImageNet M Hein, V Boreiko, M Augustin, Y Neuhaus arXiv, 2023 | | 2023 |
| The Needle in the haystack: Out-distribution aware Self-training in an Open-World Setting M Augustin, M Hein | | 2021 |
