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ORCIDRoland S. Zimmermann
Person information
- affiliation: University of Tübingen, Germany
- affiliation (former): Volkswagen AG Wolfsburg, Germany
- affiliation (former): University of Göttingen, Germany
Other persons with the same name
- Roland Zimmermann 0001
![[0000-0001-7380-5908]](https://dblp1.uni-trier.de/img/orcid-mark.12x12.png "0000-0001-7380-5908")
— Georg Simon Ohm University of Applied Sciences Nuremberg, Germany (and 1 more)
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Journal Articles
- 2020
[j3]Sebastian Herzog, Roland S. Zimmermann, Johannes Abele, Stefan Luther, Ulrich Parlitz:
Reconstructing Complex Cardiac Excitation Waves From Incomplete Data Using Echo State Networks and Convolutional Autoencoders. Frontiers Appl. Math. Stat. 6: 616584 (2020)- [j2]Jonas Rauber, Roland Zimmermann, Matthias Bethge, Wieland Brendel:
Foolbox Native: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX. J. Open Source Softw. 5(53): 2607 (2020) - 2019
- [j1]Roland S. Zimmermann, Julien N. Siems:
Faster training of Mask R-CNN by focusing on instance boundaries. Comput. Vis. Image Underst. 188 (2019)
Conference and Workshop Papers
- 2023
- [c8]Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius von Kügelgen, Wieland Brendel:
Provably Learning Object-Centric Representations. ICML 2023: 3038-3062 - [c7]Roland S. Zimmermann, Thomas Klein, Wieland Brendel:
Scale Alone Does not Improve Mechanistic Interpretability in Vision Models. NeurIPS 2023 - 2022
- [c6]Roland S. Zimmermann, Wieland Brendel, Florian Tramèr, Nicholas Carlini:
Increasing Confidence in Adversarial Robustness Evaluations. NeurIPS 2022 - 2021
- [c5]Judy Borowski, Roland Simon Zimmermann, Judith Schepers, Robert Geirhos, Thomas S. A. Wallis, Matthias Bethge, Wieland Brendel:
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization. ICLR 2021 - [c4]Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, Wieland Brendel:
Contrastive Learning Inverts the Data Generating Process. ICML 2021: 12979-12990 - [c3]Roland S. Zimmermann, Judy Borowski, Robert Geirhos, Matthias Bethge, Thomas S. A. Wallis, Wieland Brendel:
How Well do Feature Visualizations Support Causal Understanding of CNN Activations? NeurIPS 2021: 11730-11744 - 2020
- [c2]Nikhil Kapoor, Chun Yuan, Jonas Löhdefink, Roland Zimmermann, Serin Varghese, Fabian Hüger, Nico M. Schmidt, Peter Schlicht, Tim Fingscheidt:
A Self-Supervised Feature Map Augmentation (FMA) Loss and Combined Augmentations Finetuning to Efficiently Improve the Robustness of CNNs. CSCS 2020: 3:1-3:8 - [c1]Evgenia Rusak, Lukas Schott, Roland S. Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, Wieland Brendel:
A Simple Way to Make Neural Networks Robust Against Diverse Image Corruptions. ECCV (3) 2020: 53-69
Informal and Other Publications
- 2023
- [i14]Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius von Kügelgen, Wieland Brendel:
Provably Learning Object-Centric Representations. CoRR abs/2305.14229 (2023) - [i13]Roland S. Zimmermann, Sjoerd van Steenkiste, Mehdi S. M. Sajjadi, Thomas Kipf, Klaus Greff:
Sensitivity of Slot-Based Object-Centric Models to their Number of Slots. CoRR abs/2305.18890 (2023) - [i12]Robert Geirhos, Roland S. Zimmermann, Blair L. Bilodeau, Wieland Brendel, Been Kim:
Don't trust your eyes: on the (un)reliability of feature visualizations. CoRR abs/2306.04719 (2023) - [i11]Roland S. Zimmermann, Thomas Klein, Wieland Brendel:
Scale Alone Does not Improve Mechanistic Interpretability in Vision Models. CoRR abs/2307.05471 (2023) - 2022
- [i10]Roland S. Zimmermann, Wieland Brendel, Florian Tramèr, Nicholas Carlini:
Increasing Confidence in Adversarial Robustness Evaluations. CoRR abs/2206.13991 (2022) - 2021
- [i9]Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, Wieland Brendel:
Contrastive Learning Inverts the Data Generating Process. CoRR abs/2102.08850 (2021) - [i8]Roland S. Zimmermann, Judy Borowski, Robert Geirhos, Matthias Bethge, Thomas S. A. Wallis, Wieland Brendel:
How Well do Feature Visualizations Support Causal Understanding of CNN Activations? CoRR abs/2106.12447 (2021) - [i7]Roland S. Zimmermann, Lukas Schott, Yang Song, Benjamin A. Dunn, David A. Klindt:
Score-Based Generative Classifiers. CoRR abs/2110.00473 (2021) - 2020
- [i6]Evgenia Rusak, Lukas Schott, Roland S. Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, Wieland Brendel:
Increasing the robustness of DNNs against image corruptions by playing the Game of Noise. CoRR abs/2001.06057 (2020) - [i5]Judy Borowski, Roland S. Zimmermann, Judith Schepers, Robert Geirhos, Thomas S. A. Wallis, Matthias Bethge, Wieland Brendel:
Exemplary Natural Images Explain CNN Activations Better than Feature Visualizations. CoRR abs/2010.12606 (2020) - [i4]Nikhil Kapoor, Chun Yuan, Jonas Löhdefink, Roland Zimmermann, Serin Varghese, Fabian Hüger, Nico M. Schmidt, Peter Schlicht, Tim Fingscheidt:
A Self-Supervised Feature Map Augmentation (FMA) Loss and Combined Augmentations Finetuning to Efficiently Improve the Robustness of CNNs. CoRR abs/2012.01386 (2020) - 2019
- [i3]Borja Fernández-Gauna, Manuel Graña, Roland S. Zimmermann:
Simion Zoo: A Workbench for Distributed Experimentation with Reinforcement Learning for Continuous Control Tasks. CoRR abs/1904.07817 (2019) - [i2]Roland S. Zimmermann:
Comment on "Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network". CoRR abs/1907.00895 (2019) - 2018
- [i1]Roland S. Zimmermann, Julien N. Siems:
Faster Training of Mask R-CNN by Focusing on Instance Boundaries. CoRR abs/1809.07069 (2018)
Coauthor Index
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