Deep One-Class Classification. - Lukas Ruff; Nico Görnitz; Lucas Deecke; Shoaib Ahmed Siddiqui; Robert A. Vandermeulen; Alexander Binder; Emmanuel Müller; Marius Kloft (ICML, 2018)

Image Anomaly Detection with Generative Adversarial Networks. - Lucas Deecke; Robert A. Vandermeulen; Lukas Ruff; Stephan Mandt; Marius Kloft (ECML/PKDD (1), 2018)

Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on Text. - Lukas Ruff; Yury Zemlyanskiy; Robert A. Vandermeulen; Thomas Schnake; Marius Kloft (ACL (1), 2019)

Deep Semi-Supervised Anomaly Detection. - Lukas Ruff; Robert A. Vandermeulen; Nico Görnitz; Alexander Binder; Emmanuel Müller; Klaus-Robert Müller; Marius Kloft (ICLR, 2020)

Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification. - Penny Chong; Lukas Ruff; Marius Kloft; Alexander Binder (IJCNN, 2020)

Rethinking Assumptions in Deep Anomaly Detection. - Lukas Ruff; Robert A. Vandermeulen; Billy Joe Franks; Klaus-Robert Müller; Marius Kloft (CoRR, 2020)

The Clever Hans Effect in Anomaly Detection. - Jacob R. Kauffmann; Lukas Ruff; Grégoire Montavon; Klaus-Robert Müller (CoRR, 2020)

Geometric Disentanglement by Random Convex Polytopes. - Michael Joswig; Marek Kaluba; Lukas Ruff (CoRR, 2020)

Deep Anomaly Detection by Residual Adaptation. - Lucas Deecke; Lukas Ruff; Robert A. Vandermeulen; Hakan Bilen (CoRR, 2020)

Explainable Deep One-Class Classification. - Philipp Liznerski; Lukas Ruff; Robert A. Vandermeulen; Billy Joe Franks; Marius Kloft; Klaus-Robert Müller (ICLR, 2021)

Transfer-Based Semantic Anomaly Detection. - Lucas Deecke; Lukas Ruff; Robert A. Vandermeulen; Hakan Bilen (ICML, 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)

Deep one-class learning: a deep learning approach to anomaly detection. - Lukas Ruff (2021)

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)

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; et al. (NeurIPS, 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)

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; et al. (CoRR, 2024)

AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics. - Jonas Dippel; Niklas Prenißl; Julius Hense; Philipp Liznerski; Tobias Winterhoff; Simon Schallenberg; Marius Kloft; Oliver Buchstab; David Horst; Maximilian Alber; Lukas Ruff; Klaus-Robert Müller; et al. (CoRR, 2024)

The Clever Hans Effect in Unsupervised Learning. - Jacob R. Kauffmann; Jonas Dippel; Lukas Ruff; Wojciech Samek; Klaus-Robert Müller; Grégoire Montavon (CoRR, 2024)

xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell Lung Cancer. - Marvin Sextro; Gabriel Dernbach; Kai Standvoss; Simon Schallenberg; Frederick Klauschen; Klaus-Robert Müller; Maximilian Alber; Lukas Ruff (CoRR, 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., 2024)

Explainable AI Reveals Clever Hans Effects in Unsupervised Learning Models: Code. - Jacob R. Kauffmann; Jonas Dippel; Lukas Ruff; Wojciech Samek; Klaus-Robert Müller; Grégoire Montavon (Zenodo, 2024)

Explainable AI reveals Clever Hans effects in unsupervised learning models. - Jacob R. Kauffmann; Jonas Dippel; Lukas Ruff; Wojciech Samek; Klaus-Robert Müller; Grégoire Montavon (Nat. Mac. Intell., 2025)

Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charité, and Aignostics. - Maximilian Alber; Stephan Tietz; Jonas Dippel; Timo Milbich; Timothée Lesort; Panos Korfiatis; Moritz Krügener; Beatriz Perez Cancer; Neelay Shah; Alexander Möllers; Philipp Seegerer; Alexandra Carpen-Amarie; et al. (CoRR, 2025)

Towards Robust Foundation Models for Digital Pathology. - Jonah Kömen; Edwin D. de Jong; Julius Hense; Hannah Marienwald; Jonas Dippel; Philip Naumann; Eric Marcus; Lukas Ruff; Maximilian Alber; Jonas Teuwen; Frederick Klauschen; Klaus-Robert Müller (CoRR, 2025)