Wieland Brendel
Wieland Brendel
Postdoctoral 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
7182018
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
4972017
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
318*2017
On evaluating adversarial robustness
N Carlini, A Athalye, N Papernot, W Brendel, J Rauber, D Tsipras, ...
arXiv preprint arXiv:1902.06705, 2019
2952019
Demixed principal component analysis of neural population data
D Kobak, W Brendel, C Constantinidis, CE Feierstein, A Kepecs, ...
Elife 5, e10989, 2016
2442016
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
2342019
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
178*2018
On adaptive attacks to adversarial example defenses
F Tramer, N Carlini, W Brendel, A Madry
34th Conference on Neural Information Processing Systems (NeurIPS), 2020
1232020
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
1042020
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
662009
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
602019
Demixed principal component analysis
W Brendel, R Romo, CK Machens
Advances in Neural Information Processing Systems 24 (NIPS 2011), 2654-2662, 2011
512011
Adversarial vision challenge
W Brendel, J Rauber, A Kurakin, N Papernot, B Veliqi, SP Mohanty, ...
The NeurIPS'18 Competition, 129-153, 2020
382020
Texture synthesis using shallow convolutional networks with random filters
I Ustyuzhaninov, W Brendel, LA Gatys, M Bethge
arXiv preprint arXiv:1606.00021, 2016
362016
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
252020
Learning to represent signals spike by spike
W Brendel, R Bourdoukan, P Vertechi, CK Machens, S Denéve
PLoS computational biology 16 (3), e1007692, 2020
232020
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
232019
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
21*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
192020
What does it take to generate natural textures?
I Ustyuzhaninov, W Brendel, LA Gatys, M Bethge
International Conference on Learning Representations (ICLR), 2017
172017
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Articles 1–20