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Contrastive Learning Inverts the Data Generating Process

Feb 17, 2021
Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, Wieland Brendel

* The first three authors, as well as the last two authors, contributed equally. Code is available at https://brendel-group.github.io/cl-ica 

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Exemplary Natural Images Explain CNN Activations Better than Feature Visualizations

Oct 23, 2020
Judy Borowski, Roland S. Zimmermann, Judith Schepers, Robert Geirhos, Thomas S. A. Wallis, Matthias Bethge, Wieland Brendel


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On the surprising similarities between supervised and self-supervised models

Oct 16, 2020
Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Matthias Bethge, Felix A. Wichmann, Wieland Brendel


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EagerPy: Writing Code That Works Natively with PyTorch, TensorFlow, JAX, and NumPy

Aug 10, 2020
Jonas Rauber, Matthias Bethge, Wieland Brendel


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Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding

Jul 21, 2020
David Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, Dylan Paiton

* Code is available at https://github.com/bethgelab/slow_disentanglement. The first three authors, as well as the last two authors, contributed equally 

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Improving robustness against common corruptions by covariate shift adaptation

Jun 30, 2020
Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, Matthias Bethge


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Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences

Jun 12, 2020
Marissa A. Weis, Kashyap Chitta, Yash Sharma, Wieland Brendel, Matthias Bethge, Andreas Geiger, Alexander S. Ecker


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Shortcut Learning in Deep Neural Networks

May 20, 2020
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, Felix A. Wichmann

* perspective article 

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The Notorious Difficulty of Comparing Human and Machine Perception

Apr 20, 2020
Christina M. Funke, Judy Borowski, Karolina Stosio, Wieland Brendel, Thomas S. A. Wallis, Matthias Bethge


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Increasing the robustness of DNNs against image corruptions by playing the Game of Noise

Feb 26, 2020
Evgenia Rusak, Lukas Schott, Roland S. Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, Wieland Brendel


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On Adaptive Attacks to Adversarial Example Defenses

Feb 19, 2020
Florian Tramer, Nicholas Carlini, Wieland Brendel, Aleksander Madry


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Learning From Brains How to Regularize Machines

Nov 11, 2019
Zhe Li, Wieland Brendel, Edgar Y. Walker, Erick Cobos, Taliah Muhammad, Jacob Reimer, Matthias Bethge, Fabian H. Sinz, Xaq Pitkow, Andreas S. Tolias

* 14 pages, 7 figures, NeurIPS 2019 

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Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming

Jul 17, 2019
Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos, Evgenia Rusak, Oliver Bringmann, Alexander S. Ecker, Matthias Bethge, Wieland Brendel

* 23 pages, 10 figures, 1 dragon 

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Accurate, reliable and fast robustness evaluation

Jul 01, 2019
Wieland Brendel, Jonas Rauber, Matthias Kümmerer, Ivan Ustyuzhaninov, Matthias Bethge


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Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet

Mar 20, 2019
Wieland Brendel, Matthias Bethge

* Published as a conference paper at the Seventh International Conference on Learning Representations (ICLR 2019) 

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On Evaluating Adversarial Robustness

Feb 20, 2019
Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian Goodfellow, Aleksander Madry, Alexey Kurakin

* Living document; source available at https://github.com/evaluating-adversarial-robustness/adv-eval-paper/ 

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ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

Nov 29, 2018
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, Wieland Brendel

* Under review at ICLR 2019 (review scores 8,8,7) 

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Towards the first adversarially robust neural network model on MNIST

Sep 20, 2018
Lukas Schott, Jonas Rauber, Matthias Bethge, Wieland Brendel


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Adversarial Vision Challenge

Aug 06, 2018
Wieland Brendel, Jonas Rauber, Alexey Kurakin, Nicolas Papernot, Behar Veliqi, Marcel Salathé, Sharada P. Mohanty, Matthias Bethge

* https://www.crowdai.org/challenges/adversarial-vision-challenge 

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One-shot Texture Segmentation

Jul 07, 2018
Ivan Ustyuzhaninov, Claudio Michaelis, Wieland Brendel, Matthias Bethge


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Trace your sources in large-scale data: one ring to find them all

Mar 23, 2018
Alexander Böttcher, Wieland Brendel, Bernhard Englitz, Matthias Bethge


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Foolbox: A Python toolbox to benchmark the robustness of machine learning models

Mar 20, 2018
Jonas Rauber, Wieland Brendel, Matthias Bethge

* Code and examples available at https://github.com/bethgelab/foolbox and documentation available at http://foolbox.readthedocs.io 

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Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models

Feb 16, 2018
Wieland Brendel, Jonas Rauber, Matthias Bethge

* Published as a conference paper at the Sixth International Conference on Learning Representations (ICLR 2018) https://openreview.net/forum?id=SyZI0GWCZ 

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Comment on "Biologically inspired protection of deep networks from adversarial attacks"

Apr 05, 2017
Wieland Brendel, Matthias Bethge

* 4 pages, 3 figures 

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Texture Synthesis Using Shallow Convolutional Networks with Random Filters

May 31, 2016
Ivan Ustyuzhaninov, Wieland Brendel, Leon A. Gatys, Matthias Bethge

* 9 pages, 4 figures 

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Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parameters

Oct 22, 2014
Dmitry Kobak, Wieland Brendel, Christos Constantinidis, Claudia E. Feierstein, Adam Kepecs, Zachary F. Mainen, Ranulfo Romo, Xue-Lian Qi, Naoshige Uchida, Christian K. Machens

* 23 pages, 6 figures + supplementary information (21 pages, 15 figures) 

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