Wieland Brendel
Wieland Brendel
Emmy Noether Fellow, University of Tübingen
Verified email at uni-tuebingen.de
Title
Cited by
Cited by
Year
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
9752018
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
6332017
On evaluating adversarial robustness
N Carlini, A Athalye, N Papernot, W Brendel, J Rauber, D Tsipras, ...
arXiv preprint arXiv:1902.06705, 2019
4072019
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
386*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
2892019
Demixed principal component analysis of neural population data
D Kobak, W Brendel, C Constantinidis, CE Feierstein, A Kepecs, ...
Elife 5, e10989, 2016
2772016
On adaptive attacks to adversarial example defenses
F Tramer, N Carlini, W Brendel, A Madry
34th Conference on Neural Information Processing Systems (NeurIPS), 2020
2332020
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
2242020
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
212*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
952019
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
652009
Demixed principal component analysis
W Brendel, R Romo, CK Machens
Advances in Neural Information Processing Systems 24 (NIPS 2011), 2654-2662, 2011
522011
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
472020
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
432020
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
432019
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
402020
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
38*2021
Texture synthesis using shallow convolutional networks with random filters
I Ustyuzhaninov, W Brendel, LA Gatys, M Bethge
arXiv preprint arXiv:1606.00021, 2016
382016
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
292020
Adversarial vision challenge
W Brendel, J Rauber, A Kurakin, N Papernot, B Veliqi, M Salathé, ...
arXiv preprint arXiv:1808.01976, 2018
282018
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Articles 1–20