Robert Geirhos
Robert Geirhos
University of Tübingen · International Max Planck Research School for Intelligent Systems (IMPRS-IS)
Verified email at uni-tuebingen.de - Homepage
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
Oral @ International Conference on Learning Representations (ICLR 2019), 2019
9972019
Generalisation in humans and deep neural networks
R Geirhos, CR Medina Temme, J Rauber, HH Schütt, M Bethge, ...
Advances in Neural Information Processing Systems 31 (NeurIPS 2018), 2018
2622018
Shortcut Learning in Deep Neural Networks
R Geirhos, JH Jacobsen, C Michaelis, R Zemel, W Brendel, M Bethge, ...
Nature Machine Intelligence 2 (11), 665-673, 2020
2282020
Comparing deep neural networks against humans: object recognition when the signal gets weaker
R Geirhos, DHJ Janssen, HH Schütt, J Rauber, M Bethge, FA Wichmann
arXiv preprint arXiv:1706.06969, 2017
1732017
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming
C Michaelis, B Mitzkus, R Geirhos, E Rusak, O Bringmann, AS Ecker, ...
Machine Learning for Autonomous Driving Workshop (NeurIPS 2019), 2019
952019
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency
R Geirhos, K Meding, FA Wichmann
Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2020
192020
On the surprising similarities between supervised and self-supervised models
R Geirhos, K Narayanappa, B Mitzkus, M Bethge, FA Wichmann, ...
Oral @ Shared Visual Representations in Humans & Machines Workshop (NeurIPS …, 2020
132020
Methods and measurements to compare men against machines
FA Wichmann, DHJ Janssen, R Geirhos, G Aguilar, HH Schütt, ...
Electronic Imaging 2017 (14), 36-45, 2017
102017
Comparison-Based Framework for Psychophysics: Lab versus Crowdsourcing
S Haghiri, P Rubisch, R Geirhos, F Wichmann, U von Luxburg
arXiv preprint arXiv:1905.07234, 2019
52019
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization
J Borowski, RS Zimmermann, J Schepers, R Geirhos, TSA Wallis, ...
International Conference on Learning Representations (ICLR 2021), 2020
4*2020
Partial success in closing the gap between human and machine vision
R Geirhos, K Narayanappa, B Mitzkus, T Thieringer, M Bethge, ...
Oral @ Advances in Neural Information Processing Systems 34 (NeurIPS 2021), 2021
22021
Of human observers and deep neural networks: A detailed psychophysical comparison
R Geirhos, D Jannsen, H Schütt, M Bethge, FA Wichmann
17th Annual Meeting of the Vision Sciences Society (VSS 2017), 806-806, 2017
22017
Unintended cue learning: Lessons for deep learning from experimental psychology
R Geirhos, JH Jacobsen, C Michaelis, R Zemel, W Brendel, M Bethge, ...
20th Annual Meeting of the Vision Sciences Society (VSS 2020), 652-652, 2020
12020
Trivial or impossible--dichotomous data difficulty masks model differences (on ImageNet and beyond)
K Meding, LMS Buschoff, R Geirhos, FA Wichmann
arXiv preprint arXiv:2110.05922, 2021
2021
How Well do Feature Visualizations Support Causal Understanding of CNN Activations?
RS Zimmermann, J Borowski, R Geirhos, M Bethge, TSA Wallis, ...
Spotlight @ Advances in Neural Information Processing Systems 34 (NeurIPS 2021), 2021
2021
The developmental trajectory of object recognition robustness: comparing children, adults, and CNNs
LS Huber, R Geirhos, FA Wichmann
Oral @ 21st Annual Meeting of the Vision Sciences Society (VSS 2021), 1967, 2021
2021
Inducing a human-like shape bias leads to emergent human-level distortion robustness in CNNs
R Geirhos, P Rubisch, J Rauber, CRM Temme, C Michaelis, W Brendel, ...
Oral @ 19th Annual Meeting of the Vision Sciences Society (VSS 2019), 209c-209c, 2019
2019
An Automatized Heider-Simmel Story Generation Tool.
MV Butz, R Geirhos, J Kneissler
37th Annual Meeting of the Cognitive Science Society (CogSci 2015), 2015
2015
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Articles 1–18