| A simple way to make neural networks robust against diverse image corruptions E Rusak, L Schott, RS Zimmermann, J Bitterwolf, O Bringmann, M Bethge, ... ECCV 2020 (Oral), 2020 | 204* | 2020 |
| Foolbox Native: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX J Rauber, R Zimmermann, M Bethge, W Brendel Journal of Open Source Software 5 (53), 2607, 2020 | 167 | 2020 |
| Contrastive Learning Inverts the Data Generating Process RS Zimmermann, Y Sharma, S Schneider, M Bethge, W Brendel ICML 2021 (Spotlight) 139, 12979-12990, 2021 | 155 | 2021 |
| Observing spatio-temporal dynamics of excitable media using reservoir computing RS Zimmermann, U Parlitz Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (4), 2018 | 148 | 2018 |
| Faster training of Mask R-CNN by focusing on instance boundaries RS Zimmermann, JN Siems Computer Vision and Image Understanding, 102795, 2019 | 67 | 2019 |
| Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization J Borowski, RS Zimmermann, J Schepers, R Geirhos, TSA Wallis, ... ICLR 2021, 2020 | 32* | 2020 |
| Score-Based Generative Classifiers RS Zimmermann, L Schott, Y Song, BA Dunn, DA Klindt arXiv preprint arXiv:2110.00473, 2021 | 30 | 2021 |
| How Well do Feature Visualizations Support Causal Understanding of CNN Activations? RS Zimmermann, J Borowski, R Geirhos, M Bethge, T Wallis, W Brendel NeurIPS 2021 (Spotlight), 2021 | 18 | 2021 |
| Comment on "Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network" RS Zimmermann arXiv preprint arXiv:1907.00895, 2019 | 18 | 2019 |
| Reconstructing Complex Cardiac Excitation Waves From Incomplete Data Using Echo State Networks and Convolutional Autoencoders S Herzog, RS Zimmermann, J Abele, S Luther, U Parlitz Frontiers in Applied Mathematics and Statistics 6, 616584, 2021 | 6 | 2021 |
| Provably Learning Object-Centric Representations J Brady, RS Zimmermann, Y Sharma, B Schölkopf, J von Kügelgen, ... ICML 2023 (Oral), 2023 | 5 | 2023 |
| A self-supervised feature map augmentation (FMA) loss and combined augmentations finetuning to efficiently improve the robustness of CNNs N Kapoor, C Yuan, J Löhdefink, R Zimmerman, S Varghese, F Hüger, ... Proceedings of the 4th ACM Computer Science in Cars Symposium, 1-8, 2020 | 5 | 2020 |
| Increasing Confidence in Adversarial Robustness Evaluations RS Zimmermann, W Brendel, F Tramer, N Carlini NeurIPS 2022, 2022 | 4 | 2022 |
| Scale Alone Does not Improve Mechanistic Interpretability in Vision Models RS Zimmermann, T Klein, W Brendel NeurIPS 2023 (Spotlight), 2023 | 3 | 2023 |
| Don't trust your eyes: on the (un) reliability of feature visualizations R Geirhos, RS Zimmermann, B Bilodeau, W Brendel, B Kim arXiv preprint arXiv:2306.04719, 2023 | 3 | 2023 |
| Simion Zoo: A Workbench for Distributed Experimentation with Reinforcement Learning for Continuous Control Tasks B Fernandez-Gauna, M Graña, RS Zimmermann arXiv preprint arXiv:1904.07817, 2019 | 1 | 2019 |
| Sensitivity of Slot-Based Object-Centric Models to their Number of Slots RS Zimmermann, S van Steenkiste, MSM Sajjadi, T Kipf, K Greff arXiv preprint arXiv:2305.18890, 2023 | | 2023 |
| Content suppresses style: dimensionality collapse in contrastive learning E Rusak, P Reizinger, RS Zimmermann, O Bringmann, W Brendel NeurIPS 2022 Workshop: Self-Supervised Learning - Theory and Practice, 2022 | | 2022 |
Ulrich ParlitzMax Planck Institute for Dynamics and Self-Organization, GöttingenVerified email at ds.mpg.de
