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 | 1318 | 2018 |
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 | 808 | 2017 |
On evaluating adversarial robustness N Carlini, A Athalye, N Papernot, W Brendel, J Rauber, D Tsipras, ... arXiv preprint arXiv:1902.06705, 2019 | 522 | 2019 |
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 | 462* | 2017 |
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 | 438 | 2020 |
On adaptive attacks to adversarial example defenses F Tramer, N Carlini, W Brendel, A Madry 34th Conference on Neural Information Processing Systems (NeurIPS), 2020 | 372 | 2020 |
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 | 372 | 2019 |
Demixed principal component analysis of neural population data D Kobak, W Brendel, C Constantinidis, CE Feierstein, A Kepecs, ... Elife 5, e10989, 2016 | 334 | 2016 |
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 | 272 | 2018 |
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 | 150 | 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 | 95 | 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, ... European Conference on Computer Vision, 53-69, 2020 | 81 | 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 | 73 | 2020 |
Instanton constituents and fermionic zero modes in twisted CPn models W Brendel, F Bruckmann, L Janssen, A Wipf, C Wozar Physics Letters B 676 (1-3), 116-125, 2009 | 68 | 2009 |
Demixed principal component analysis W Brendel, R Romo, CK Machens Advances in Neural Information Processing Systems 24 (NIPS 2011), 2654-2662, 2011 | 58 | 2011 |
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 | 57 | 2019 |
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 | 55* | 2021 |
Texture synthesis using shallow convolutional networks with random filters I Ustyuzhaninov, W Brendel, LA Gatys, M Bethge arXiv preprint arXiv:1606.00021, 2016 | 39 | 2016 |
Increasing the robustness of DNNs against image corruptions by playing the Game of Noise E Rusak, L Schott, R Zimmermann, J Bitterwolf, O Bringmann, M Bethge, ... European Conference on Computer Vision (oral), 2020 | 33 | 2020 |
Learning to represent signals spike by spike W Brendel, R Bourdoukan, P Vertechi, CK Machens, S Denéve PLoS computational biology 16 (3), e1007692, 2020 | 32 | 2020 |