Publications by W. Brendel

Preprints


R. S. Zimmermann, Y. Sharma, S. Schneider, M. Bethge, and W. Brendel
Contrastive Learning Inverts the Data Generating Process
arXiv, 2021
Code, URL, BibTex
J. Borowski, R. S. Zimmermann, J. Schepers, R. Geirhos, T. S. A. Wallis, M. Bethge, and W. Brendel
Exemplary Natural Images Explain CNN Activations Better than Feature Visualizations
arXiv, 2020
Code, URL, BibTex
J. Rauber, M. Bethge, and W. Brendel
EagerPy: Writing Code That Works Natively with PyTorch, TensorFlow, JAX, and NumPy
2020
URL, BibTex
D. Klindt, L. Schott, Y. Sharma, I. Ustyuzhaninov, W. Brendel, M. Bethge, and D. Paiton
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding
arXiv, 2020
#disentanglement, #independent component analysis, #nonlinear, #deep learning, #computer vision, #benchmarking
Code, URL, BibTex
S. Schneider, E. Rusak, L. Eck, O. Bringmann, W. Brendel, and M. Bethge
Removing covariate shift improves robustness against common corruptions
arXiv, 2020
#domain adaptation, #covariate shift, #robustness, #deep learning, #computer vision, #benchmarking
Code, URL, Workshop Paper, BibTex
M. A. Weis, K. Chitta, Y. Sharma, W. Brendel, M. Bethge, A. Geiger, and A. S. Ecker
Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences
arXiv, 2020
Code, URL, BibTex
C. M. Funke, J. Borowski, K. Stosio, W. Brendel, T. S. A. Wallis, and M. Bethge
The Notorious Difficulty of Comparing Human and Machine Perception
arXiv (superseded by "Five Points to Check when Comparing Visual Perception in Humans and Machines"), 2020
Code, URL, BibTex
I. Ustyuzhaninov, C. Michaelis, W. Brendel, and M. Bethge
One-shot Texture Segmentation
arXiv, 2018
Code, 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

Journal Articles


C. M. Funke, J. Borowski, K. Stosio, W. Brendel, T. S. A. Wallis, and M. Bethge
Five Points to Check when Comparing Visual Perception in Humans and Machines
Journal of Vision, 2021
Code, URL, BibTex
R. Geirhos, J.-H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F. A. Wichmann
Shortcut Learning in Deep Neural Networks
Nature Machine Intelligence, 2, 665-673, 2020
Code, URL, DOI, BibTex
J. Rauber, R. Zimmermann, M. Bethge, and W. Brendel
Foolbox Native: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX
Journal of Open Source Software, 5(53), 2607, 2020
URL, DOI, BibTex
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


E. Rusak, L. Schott, R. Zimmermann, J. Bitterwolf, O. Bringmann, M. Bethge, and W. Brendel
A simple way to make neural networks robust against diverse image corruptions
European Conference on Computer Vision (ECCV), 2020
URL, BibTex
R. Geirhos, K. Narayanappa, B. Mitzkus, M. Bethge, F. A. Wichmann, and W. Brendel
On the surprising similarities between supervised and self-supervised models
Shared Visual Representations in Humans & Machines Workshop, NeurIPS 2020, 2020
URL, BibTex
W. Brendel, J. Rauber, M. Kümmerer, I. Ustyuzhaninov, and M. Bethge
Accurate, reliable and fast robustness evaluation
Advances in Neural Information Processing Systems 32, 2019, 2019
URL, BibTex
C. Michaelis, B. Mitzkus, R. Geirhos, E. Rusak, O. Bringmann, A. S. Ecker, M. Bethge, and W. Brendel
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming
Machine Learning for Autonomous Driving Workshop, NeurIPS 2019, 2019
Code, URL, BibTex
W. Brendel and M. Bethge
Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet
International Conference on Learning Representations (ICLR), 2019
BibTex
R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
International Conference on Learning Representations (ICLR), 2019
Code, URL, BibTex
L. Schott, J. Rauber, W. Brendel, and M. Bethge
Towards the first adversarially robust neural network model on MNIST
International Conference on Learning Representations (ICLR), 2019
URL, BibTex
W. Brendel, J. Rauber, A. Kurakin, N. Papernot, B. Veliqi, M. Salathé, S. P. Mohanty, and M. Bethge
Adversarial Vision Challenge (Proposal)
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

Technical Reports


N. Carlini, A. Athalye, N. Papernot, W. Brendel, J. Rauber, D. Tsipras, I. Goodfellow, and A. Madry
On Evaluating Adversarial Robustness
2019
Code, URL, BibTex

Book Chapters


W. Brendel, J. Rauber, A. Kurakin, N. Papernot, B. Veliqi, S. P. Mohanty, F. Laurent, M. Salathé, et al.
Adversarial Vision Challenge (Results)
The NeurIPS'18 Competition, Springer, Cham, 2020, ISBN 978-3-030-29135-8
URL, DOI, ISBN, BibTex

Abstracts


R. Geirhos, J.-H. Jacobsen, C. Michaelis, R. Zemel, W. Brendel, M. Bethge, and F. A. Wichmann
Unintended cue learning: Lessons for deep learning from experimental psychology
Journal of Vision, 20(11), 652, 2020
DOI, BibTex
J. Borowski, C. M. Funke, K. Stosio, W. Brendel, T. S. A. Wallis, and M. Bethge
The Notorious Difficulty of Comparing Human and Machine Perception
NeurIPS Workshop: Shared Visual Representations in Human & Machine Intelligence (Best Paper Award), 2019
URL, BibTex
J. Borowski, C. M. Funke, K. Stosio, W. Brendel, T. S. A. Wallis, and M. Bethge
The Notorious Difficulty of Comparing Human and Machine Perception
Conference on Cognitive Computational Neuroscience, 2019
DOI, BibTex
R. Geirhos, P. Rubisch, J. Rauber, C. R. M. Temme, C. Michaelis, W. Brendel, M. Bethge, and F. A. Wichmann
Inducing a human-like shape bias leads to emergent human-level distortion robustness in CNNs
Journal of Vision, 19(10), 2019
DOI, BibTex
C. Funke, J. Borowski, T. Wallis, W. Brendel, A. Ecker, and M. Bethge
Comparing the ability of humans and DNNs to recognise closed contours in cluttered images
Journal of Vision, 18(10), 2018
DOI, BibTex
University of Tuebingen BCCN CIN MPI