| 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 | 3028 | 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 | 1247 | 2021 |
| Deep Semi-Supervised Anomaly Detection L Ruff, RA Vandermeulen, N Görnitz, A Binder, E Müller, KR Müller, ... International Conference on Learning Representations, 2020 | 858 | 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 | 332 | 2018 |
| Explainable Deep One-Class Classification P Liznerski, L Ruff, RA Vandermeulen, BJ Franks, M Kloft, KR Müller International Conference on Learning Representations, 2021 | 302 | 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 | 134 | 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 | 122 | 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 | 98 | 2019 |
| 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 | 88 | 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 | 60 | 2022 |
| 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 | 59 | 2020 |
| Transfer-Based Semantic Anomaly Detection L Deecke, L Ruff, RA Vandermeulen, H Bilen International Conference on Machine Learning, 2546-2558, 2021 | 48 | 2021 |
| 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 | 47 | 2024 |
| The Clever Hans Effect in Anomaly Detection J Kauffmann, L Ruff, G Montavon, KR Müller arXiv preprint arXiv:2006.10609, 2020 | 45 | 2020 |
| 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 | 35 | 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 | 24 | 2019 |
| Combining spatial transcriptomics and ECM imaging in 3D for mapping cellular interactions in the tumor microenvironment TM Pentimalli, S Schallenberg, D León-Periñán, I Legnini, I Theurillat, ... Cell Systems, 2025 | 13* | 2025 |
| Explainable AI reveals Clever Hans effects in unsupervised learning models J Kauffmann, J Dippel, L Ruff, W Samek, KR Müller, G Montavon Nature Machine Intelligence, 1-11, 2025 | 11 | 2025 |
| Dissecting AI-based mutation prediction in lung adenocarcinoma: A comprehensive real-world study G Dernbach, D Kazdal, L Ruff, M Alber, E Romanovsky, S Schallenberg, ... European Journal of Cancer 211, 114292, 2024 | 11 | 2024 |
| Deep Anomaly Detection by Residual Adaptation L Deecke, L Ruff, RA Vandermeulen, H Bilen arXiv preprint arXiv:2010.02310, 2020 | 9 | 2020 |
Robert A. VandermeulenIndependent ResearcherПодтвержден адрес электронной почты в домене umich.edu
Klaus-Robert MüllerTU Berlin & Korea University & Google DeepMind & Max Planck Institute for Informatics, GermanyПодтвержден адрес электронной почты в домене tu-berlin.de
Marius KloftProfessor, RPTU Kaiserslautern-LandauПодтвержден адрес электронной почты в домене cs.uni-kl.de
Lucas DeeckeUniversity of EdinburghПодтвержден адрес электронной почты в домене ed.ac.uk
Grégoire MontavonProfessor, Charité / BIFOLDПодтвержден адрес электронной почты в домене charite.de
Alexander BinderProfessor, ScaDS.AI and Faculty of Math and Computer Science, Uni Leipzig, GermanyПодтвержден адрес электронной почты в домене uni-leipzig.de
Nico GoernitzFreelancer | CTO | ex-postdoc@TU-BerlinПодтвержден адрес электронной почты в домене tu-berlin.de
Maximilian AlberTU BerlinПодтвержден адрес электронной почты в домене tu-berlin.de
Wojciech SamekProfessor at TU Berlin, Head of AI Department at Fraunhofer HHI, BIFOLD FellowПодтвержден адрес электронной почты в домене tu-berlin.de
Jonas DippelTU BerlinПодтвержден адрес электронной почты в домене tu-berlin.de
Stephan MandtAssociate Professor, University of California, IrvineПодтвержден адрес электронной почты в домене uci.edu
Kai StandvossEinstein Center for Neurosciences BerlinПодтвержден адрес электронной почты в домене charite.de
