| Deep One-Class Classification L Ruff, R Vandermeulen, N Görnitz, L Deecke, SA Siddiqui, A Binder, ... International Conference on Machine Learning 80, 4393-4402, 2018 | 2709 | 2018 |
| A Unifying Review of Deep and Shallow Anomaly Detection L Ruff, JR Kauffmann, RA Vandermeulen, G Montavon, W Samek, M Kloft, ... Proceedings of the IEEE, 2021 | 1092 | 2021 |
| Deep Semi-Supervised Anomaly Detection L Ruff, RA Vandermeulen, N Görnitz, A Binder, E Müller, KR Müller, ... International Conference on Learning Representations, 2020 | 773 | 2020 |
| Image Anomaly Detection with Generative Adversarial Networks L Deecke, R Vandermeulen, L Ruff, S Mandt, M Kloft Joint European Conference on Machine Learning and Knowledge Discovery in …, 2018 | 305 | 2018 |
| Explainable Deep One-Class Classification P Liznerski, L Ruff, RA Vandermeulen, BJ Franks, M Kloft, KR Müller International Conference on Learning Representations, 2021 | 281 | 2021 |
| From Clustering to Cluster Explanations via Neural Networks J Kauffmann, M Esders, L Ruff, G Montavon, W Samek, KR Müller IEEE Transactions on Neural Networks and Learning Systems, 1-15, 2022 | 119 | 2022 |
| Rethinking Assumptions in Deep Anomaly Detection L Ruff, RA Vandermeulen, BJ Franks, KR Müller, M Kloft ICML 2021 Workshop on Uncertainty and Robustness in Deep Learning, 2021 | 108 | 2021 |
| Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on Text L Ruff, Y Zemlyanskiy, R Vandermeulen, T Schnake, M Kloft Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019 | 90 | 2019 |
| Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification P Chong, L Ruff, M Kloft, A Binder International Joint Conference on Neural Networks (IJCNN), 1-9, 2020 | 56 | 2020 |
| Toward explainable artificial intelligence for precision pathology F Klauschen, J Dippel, P Keyl, P Jurmeister, M Bockmayr, A Mock, ... Annual Review of Pathology: Mechanisms of Disease 19 (1), 541-570, 2024 | 52 | 2024 |
| Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images P Liznerski, L Ruff, RA Vandermeulen, BJ Franks, KR Müller, M Kloft Transactions on Machine Learning Research, 2022 | 46 | 2022 |
| Transfer-Based Semantic Anomaly Detection L Deecke, L Ruff, RA Vandermeulen, H Bilen International Conference on Machine Learning, 2546-2558, 2021 | 45 | 2021 |
| The Clever Hans Effect in Anomaly Detection J Kauffmann, L Ruff, G Montavon, KR Müller arXiv preprint arXiv:2006.10609, 2020 | 44 | 2020 |
| RudolfV: a foundation model by pathologists for pathologists J Dippel, B Feulner, T Winterhoff, T Milbich, S Tietz, S Schallenberg, ... arXiv preprint arXiv:2401.04079, 2024 | 24 | 2024 |
| DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology M Aversa, G Nobis, M Hägele, K Standvoss, M Chirica, R Murray-Smith, ... Advances in Neural Information Processing Systems 36, 78126-78141, 2023 | 22 | 2023 |
| Deep Support Vector Data Description for Unsupervised and Semi-Supervised Anomaly Detection L Ruff, RA Vandermeulen, N Görnitz, A Binder, E Müller, M Kloft ICML 2019 Workshop on Uncertainty and Robustness in Deep Learning, 2019 | 22 | 2019 |
| High-resolution molecular atlas of a lung tumor in 3D TM Pentimalli, S Schallenberg, D León-Periñán, I Legnini, I Theurillat, ... bioRxiv, 2023.05. 10.539644, 2023 | 9 | 2023 |
| Deep Anomaly Detection by Residual Adaptation L Deecke, L Ruff, RA Vandermeulen, H Bilen arXiv preprint arXiv:2010.02310, 2020 | 9 | 2020 |
| Deep One-Class Learning: A Deep Learning Approach to Anomaly Detection L Ruff Technische Universität Berlin, 2021 | 5 | 2021 |
| Geometric Disentanglement by Random Convex Polytopes M Joswig, M Kaluba, L Ruff arXiv preprint arXiv:2009.13987, 2020 | 5 | 2020 |
Robert A. VandermeulenIndependent ResearcherVerified email at umich.edu