| ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness R Geirhos, P Rubisch, C Michaelis, M Bethge, FA Wichmann, W Brendel Seventh International Conference on Learning Representations (ICLR 2019), 2018 | 3068 | 2018 |
| Shortcut Learning in Deep Neural Networks R Geirhos, JH Jacobsen, C Michaelis, R Zemel, W Brendel, M Bethge, ... Nature Machine Intelligence volume 2, pages665–673(2020), 2020 | 1970 | 2020 |
| Decision-based adversarial attacks: Reliable attacks against black-box machine learning models W Brendel, J Rauber, M Bethge Sixth International Conference on Learning Representations (ICLR 2018), 2017 | 1587 | 2017 |
| On evaluating adversarial robustness N Carlini, A Athalye, N Papernot, W Brendel, J Rauber, D Tsipras, ... arXiv preprint arXiv:1902.06705, 2019 | 1005 | 2019 |
| On adaptive attacks to adversarial example defenses F Tramer, N Carlini, W Brendel, A Madry 34th Conference on Neural Information Processing Systems (NeurIPS), 2020 | 902 | 2020 |
| Foolbox v0. 8.0: A python toolbox to benchmark the robustness of machine learning models J Rauber, W Brendel, M Bethge Reliable Machine Learning in the Wild Workshop, 34th International …, 2017 | 720* | 2017 |
| Approximating cnns with bag-of-local-features models works surprisingly well on imagenet W Brendel, M Bethge Seventh International Conference on Learning Representations (ICLR 2019), 2019 | 692 | 2019 |
| Demixed principal component analysis of neural population data D Kobak, W Brendel, C Constantinidis, CE Feierstein, A Kepecs, ... elife 5, e10989, 2016 | 526 | 2016 |
| Benchmarking robustness in object detection: Autonomous driving when winter is coming C Michaelis, B Mitzkus, R Geirhos, E Rusak, O Bringmann, AS Ecker, ... NeurIPS 2019 Workshop on Machine Learning for Autonomous Driving, 2019 | 485 | 2019 |
| Improving robustness against common corruptions by covariate shift adaptation S Schneider, E Rusak, L Eck, O Bringmann, W Brendel, M Bethge 34th Conference on Neural Information Processing Systems (NeurIPS), 2020 | 473 | 2020 |
| Towards the first adversarially robust neural network model on MNIST L Schott, J Rauber, M Bethge, W Brendel Seventh International Conference on Learning Representations (ICLR 2019), 2018 | 444 | 2018 |
| Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style J von Kügelgen, Y Sharma, L Gresele, W Brendel, B Schölkopf, ... 35th Conference on Neural Information Processing Systems (NeurIPS), 2021 | 281 | 2021 |
| Partial success in closing the gap between human and machine vision R Geirhos, K Narayanappa, B Mitzkus, T Thieringer, M Bethge, ... 35th Conference on Neural Information Processing Systems (NeurIPS), 2021 | 226 | 2021 |
| 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 | 217 | 2020 |
| A simple way to make neural networks robust against diverse image corruptions E Rusak, L Schott, RS Zimmermann, J Bitterwolf, O Bringmann, M Bethge, ... Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 216 | 2020 |
| Contrastive Learning Inverts the Data Generating Process RS Zimmermann, Y Sharma, S Schneider, M Bethge, W Brendel International Conference on Machine Learning (ICML 2021), 2021 | 213 | 2021 |
| Accurate, reliable and fast robustness evaluation W Brendel, J Rauber, M Kümmerer, I Ustyuzhaninov, M Bethge 33rd Conference on Neural Information Processing Systems (NeurIPS), 12841-12851, 2019 | 134 | 2019 |
| Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding D Klindt, L Schott, Y Sharma, I Ustyuzhaninov, W Brendel, M Bethge, ... International Conference on Learning Representations (ICLR), 2020 | 129 | 2020 |
| Five points to check when comparing visual perception in humans and machines CM Funke, J Borowski, K Stosio, W Brendel, TSA Wallis, M Bethge Journal of Vision 21 (3), 16-16, 2021 | 97* | 2021 |
| Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints M Pintor, F Roli, W Brendel, B Biggio 35th Conference on Neural Information Processing Systems (NeurIPS), 2021 | 74 | 2021 |
Matthias BethgeTübingen University & Maddox Co-FounderVerified email at bethgelab.org
