Publications by W. Brendel

Preprints


R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel
ImageNet-trained CNNs are biased towards texture; icreasing shape bias improves accuracy and robustness
arXiv, 2018
Code, URL, BibTex
I. Ustyuzhaninov, C. Michaelis, W. Brendel, and M. Bethge
One-shot Texture Segmentation
arXiv, 2018
URL, BibTex
A. Böttcher, W. Brendel, B. Englitz, and M. Bethge
Trace your sources in large-scale data: one ring to find them all
arXiv, 2018
Code, URL, BibTex
L. Schott, J. Rauber, W. Brendel, and M. Bethge
Towards the first adversarially robust neural network model on MNIST
arXiv, 2018
URL, BibTex

Journal Articles


D. Kobak, W. Brendel, C. Constantinidis, C. E. Feierstein, A. Kepecs, Z. F. Mainen, R. Romo, X.-L. Qi, et al.
Demixed principal component analysis of neural population data
eLife, 5, 2016
URL, DOI, BibTex

Conference Papers


W. Brendel, J. Rauber, A. Kurakin, N. Papernot, B. Veliqi, M. Salath\'e, S. P. Mohanty, and M. Bethge
Adversarial Vision Challenge
32nd Conference on Neural Information Processing Systems (NIPS 2018) Competition Track, 2018
Code, URL, BibTex
W. Brendel, J. Rauber, and M. Bethge
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models
International Conference on Learning Representations, 2018
#adversarial attacks, #adversarial examples, #adversarials
Code, URL, OpenReview, BibTex
J. Rauber, W. Brendel, and M. Bethge
Foolbox: A Python toolbox to benchmark the robustness of machine learning models
Reliable Machine Learning in the Wild Workshop, 34th International Conference on Machine Learning, 2017
#adversarial attacks, #adversarial examples, #adversarials
Code, URL, BibTex
University of Tuebingen BCCN CIN MPI