Zimmermann, Roland S.

19 publications

ICLR 2025 In Search of Forgotten Domain Generalization Prasanna Mayilvahanan, Roland S. Zimmermann, Thaddäus Wiedemer, Evgenia Rusak, Attila Juhos, Matthias Bethge, Wieland Brendel

ICLRW 2025 In Search of Forgotten Domain Generalization Prasanna Mayilvahanan, Roland S. Zimmermann, Thaddäus Wiedemer, Evgenia Rusak, Attila Juhos, Matthias Bethge, Wieland Brendel

AISTATS 2025 InfoNCE: Identifying the Gap Between Theory and Practice Evgenia Rusak, Patrik Reizinger, Attila Juhos, Oliver Bringmann, Roland S. Zimmermann, Wieland Brendel

ICML 2025 LAION-C: An Out-of-Distribution Benchmark for Web-Scale Vision Models Fanfei Li, Thomas Klein, Wieland Brendel, Robert Geirhos, Roland S. Zimmermann

ICLRW 2025 LAION-C: An Out-of-Distribution Benchmark for Web-Scale Vision Models Fanfei Li, Thomas Klein, Wieland Brendel, Robert Geirhos, Roland S. Zimmermann

ICML 2024 Don’t Trust Your Eyes: On the (un)reliability of Feature Visualizations Robert Geirhos, Roland S. Zimmermann, Blair Bilodeau, Wieland Brendel, Been Kim

ICMLW 2024 In Search of Forgotten Domain Generalization Prasanna Mayilvahanan, Roland S. Zimmermann, Thaddäus Wiedemer, Evgenia Rusak, Attila Juhos, Matthias Bethge, Wieland Brendel

ICMLW 2024 InfoNCE: Identifying the Gap Between Theory and Practice Evgenia Rusak, Patrik Reizinger, Attila Juhos, Oliver Bringmann, Roland S. Zimmermann, Wieland Brendel

ICMLW 2024 InfoNCE: Identifying the Gap Between Theory and Practice Evgenia Rusak, Patrik Reizinger, Attila Juhos, Oliver Bringmann, Roland S. Zimmermann, Wieland Brendel

ICLRW 2024 Measuring Mechanistic Interpretability at Scale Without Humans Roland S. Zimmermann, David A. Klindt, Wieland Brendel

NeurIPS 2024 Measuring Per-Unit Interpretability at Scale Without Humans Roland S. Zimmermann, David Klindt, Wieland Brendel

ICMLW 2023 Don't Trust Your Eyes: On the (un)reliability of Feature Visualizations Robert Geirhos, Roland S. Zimmermann, Blair Bilodeau, Wieland Brendel, Been Kim

ICML 2023 Provably Learning Object-Centric Representations Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius Von Kügelgen, Wieland Brendel

NeurIPS 2023 Scale Alone Does Not Improve Mechanistic Interpretability in Vision Models Roland S. Zimmermann, Thomas Klein, Wieland Brendel

NeurIPS 2022 Increasing Confidence in Adversarial Robustness Evaluations Roland S. Zimmermann, Wieland Brendel, Florian Tramer, Nicholas Carlini

ICML 2021 Contrastive Learning Inverts the Data Generating Process Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, Wieland Brendel

NeurIPS 2021 How Well Do Feature Visualizations Support Causal Understanding of CNN Activations? Roland S. Zimmermann, Judy Borowski, Robert Geirhos, Matthias Bethge, Thomas Wallis, Wieland Brendel

NeurIPSW 2021 Score-Based Generative Classifiers Roland S. Zimmermann, Lukas Schott, Yang Song, Benjamin Adric Dunn, David A. Klindt

ECCV 2020 A Simple Way to Make Neural Networks Robust Against Diverse Image Corruptions Evgenia Rusak, Lukas Schott, Roland S. Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, Wieland Brendel