| 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 | 997 | 2019 |
| 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 | 262 | 2018 |
| 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 | 228 | 2020 |
| 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 | 173 | 2017 |
| 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 | 95 | 2019 |
| 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 | 19 | 2020 |
| 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 | 13 | 2020 |
| 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 | 10 | 2017 |
| 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 | 5 | 2019 |
| 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 | 2 | 2021 |
| 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 | 2 | 2017 |
| 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 | 1 | 2020 |
| 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 |
Matthias BethgeTübingen University & Amazon ScholarVerified email at bethgelab.org
