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Lukas Ruff
Lukas Ruff
Aignostics
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Zitiert von
Zitiert von
Jahr
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
25942018
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
10322021
Deep Semi-Supervised Anomaly Detection
L Ruff, RA Vandermeulen, N Görnitz, A Binder, E Müller, KR Müller, ...
International Conference on Learning Representations, 2020
7432020
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
2982018
Explainable Deep One-Class Classification
P Liznerski, L Ruff, RA Vandermeulen, BJ Franks, M Kloft, KR Müller
International Conference on Learning Representations, 2021
2692021
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
1142022
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
1052021
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
832019
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
512020
The Clever Hans Effect in Anomaly Detection
J Kauffmann, L Ruff, G Montavon, KR Müller
arXiv preprint arXiv:2006.10609, 2020
442020
Transfer-Based Semantic Anomaly Detection
L Deecke, L Ruff, RA Vandermeulen, H Bilen
International Conference on Machine Learning, 2546-2558, 2021
422021
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
412024
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
412022
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
222024
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
222019
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
172023
Deep Anomaly Detection by Residual Adaptation
L Deecke, L Ruff, RA Vandermeulen, H Bilen
arXiv preprint arXiv:2010.02310, 2020
92020
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
72023
Deep One-Class Learning: A Deep Learning Approach to Anomaly Detection
L Ruff
Technische Universität Berlin, 2021
52021
Geometric Disentanglement by Random Convex Polytopes
M Joswig, M Kaluba, L Ruff
arXiv preprint arXiv:2009.13987, 2020
52020
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