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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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Towards causal generative scene models via competition of experts

Apr 27, 2020
Julius von K√ľgelgen, Ivan Ustyuzhaninov, Peter Gehler, Matthias Bethge, Bernhard Sch√∂lkopf

* Presented at the ICLR 2020 workshop "Causal learning for decision making" 

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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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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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Pretraining boosts out-of-domain robustness for pose estimation

Sep 24, 2019
Alexander Mathis, Mert Y√ľksekg√∂n√ľl, Byron Rogers, Matthias Bethge, Mackenzie W. Mathis


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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 K√ľmmerer, 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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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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One-Shot Instance Segmentation

Nov 28, 2018
Claudio Michaelis, Ivan Ustyuzhaninov, Matthias Bethge, Alexander S. Ecker


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Excessive Invariance Causes Adversarial Vulnerability

Nov 01, 2018
Jörn-Henrik Jacobsen, Jens Behrmann, Richard Zemel, Matthias Bethge


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A rotation-equivariant convolutional neural network model of primary visual cortex

Sep 27, 2018
Alexander S. Ecker, Fabian H. Sinz, Emmanouil Froudarakis, Paul G. Fahey, Santiago A. Cadena, Edgar Y. Walker, Erick Cobos, Jacob Reimer, Andreas S. Tolias, Matthias Bethge


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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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Generalisation in humans and deep neural networks

Aug 27, 2018
Robert Geirhos, Carlos R. Medina Temme, Jonas Rauber, Heiko H. Schuett, Matthias Bethge, Felix A. Wichmann

* Submitted to NIPS 2018. 26 pages, 14 figures, 1 table. Supersedes and greatly extends arXiv:1706.06969 

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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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Diverse feature visualizations reveal invariances in early layers of deep neural networks

Jul 27, 2018
Santiago A. Cadena, Marissa A. Weis, Leon A. Gatys, Matthias Bethge, Alexander S. Ecker

* Accepted for ECCV 2018 

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Saliency Benchmarking Made Easy: Separating Models, Maps and Metrics

Jul 25, 2018
Matthias K√ľmmerer, Thomas S. A. Wallis, Matthias Bethge

* published at ECCV 2018 

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One-shot Texture Segmentation

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


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One-Shot Segmentation in Clutter

Jun 13, 2018
Claudio Michaelis, Matthias Bethge, Alexander S. Ecker

* To appaer in: $\textit{Proceedings of the $\mathit{35}^{th}$ International Conference on Machine Learning}$, Stockholm, Sweden, PMLR 80, 2018 

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Markerless tracking of user-defined features with deep learning

Apr 09, 2018
Alexander Mathis, Pranav Mamidanna, Taiga Abe, Kevin M. Cury, Venkatesh N. Murthy, Mackenzie W. Mathis, Matthias Bethge

* Nature Neuroscience, Technical Report, published: 20 August 2018 
* Videos at http://www.mousemotorlab.org/deeplabcut 

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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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