Lukas Ruff

According to our database, Lukas Ruff authored at least 18 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

From Clustering to Cluster Explanations via Neural Networks.

Jacob R. Kauffmann, Malte Esders, Lukas Ruff, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller

IEEE Trans. Neural Networks Learn. Syst., February, 2024

RudolfV: A Foundation Model by Pathologists for Pathologists.

Jonas Dippel, Barbara Feulner, Tobias Winterhoff, Simon Schallenberg, Gabriel Dernbach, Andreas Kunft, Stephan Tietz, Philipp Jurmeister, David Horst, Lukas Ruff, Klaus-Robert Müller, Frederick Klauschen, Maximilian Alber

CoRR, 2024

2023

Leveraging weak complementary labels to improve semantic segmentation of hepatocellular carcinoma and cholangiocarcinoma in H&E-stained slides.

Miriam Hägele, Johannes Eschrich, Lukas Ruff, Maximilian Alber, Simon Schallenberg, Adrien Guillot, Christoph Roderburg, Frank Tacke, Frederick Klauschen

CoRR, 2023

DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology.

Marco Aversa, Gabriel Nobis, Miriam Hägele, Kai Standvoss, Mihaela Chirica, Roderick Murray-Smith, Ahmed M. Alaa, Lukas Ruff, Daniela Ivanova, Wojciech Samek, Frederick Klauschen, Bruno Sanguinetti, Luis Oala

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images.

Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Klaus-Robert Müller, Marius Kloft

Trans. Mach. Learn. Res., 2022

2021

Deep one-class learning: a deep learning approach to anomaly detection.

Lukas Ruff

PhD thesis, 2021

A Unifying Review of Deep and Shallow Anomaly Detection.

Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, Klaus-Robert Müller

Proc. IEEE, 2021

Transfer-Based Semantic Anomaly Detection.

Lucas Deecke, Lukas Ruff, Robert A. Vandermeulen, Hakan Bilen

Proceedings of the 38th International Conference on Machine Learning, 2021

Explainable Deep One-Class Classification.

Philipp Liznerski, Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Marius Kloft, Klaus-Robert Müller

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Deep Anomaly Detection by Residual Adaptation.

Lucas Deecke, Lukas Ruff, Robert A. Vandermeulen, Hakan Bilen

CoRR, 2020

Geometric Disentanglement by Random Convex Polytopes.

Michael Joswig, Marek Kaluba, Lukas Ruff

CoRR, 2020

The Clever Hans Effect in Anomaly Detection.

Jacob R. Kauffmann, Lukas Ruff, Grégoire Montavon, Klaus-Robert Müller

CoRR, 2020

Rethinking Assumptions in Deep Anomaly Detection.

Lukas Ruff, Robert A. Vandermeulen, Billy Joe Franks, Klaus-Robert Müller, Marius Kloft

CoRR, 2020

Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification.

Penny Chong, Lukas Ruff, Marius Kloft, Alexander Binder

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Deep Semi-Supervised Anomaly Detection.

Lukas Ruff, Robert A. Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, Marius Kloft

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on Text.

Lukas Ruff, Yury Zemlyanskiy, Robert A. Vandermeulen, Thomas Schnake, Marius Kloft

Proceedings of the 57th Conference of the Association for Computational Linguistics, 2019

2018

Image Anomaly Detection with Generative Adversarial Networks.

Lucas Deecke, Robert A. Vandermeulen, Lukas Ruff, Stephan Mandt, Marius Kloft

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018

Deep One-Class Classification.

Lukas Ruff, Nico Görnitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Robert A. Vandermeulen, Alexander Binder, Emmanuel Müller, Marius Kloft

Proceedings of the 35th International Conference on Machine Learning, 2018