| 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 arXiv preprint arXiv:1811.12231, 2018 | 3207 | 2018 |
| The psychometric function: I. Fitting, sampling, and goodness of fit FA Wichmann, NJ Hill Perception & psychophysics 63 (8), 1293-1313, 2001 | 2820 | 2001 |
| 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 | 2116 | 2020 |
| The psychometric function: II. Bootstrap-based confidence intervals and sampling FA Wichmann, NJ Hill Perception & psychophysics 63, 1314-1329, 2001 | 991 | 2001 |
| Generalisation in humans and deep neural networks R Geirhos, CRM Temme, J Rauber, HH Schütt, M Bethge, FA Wichmann Advances in neural information processing systems 31, 2018 | 754 | 2018 |
| The contributions of color to recognition memory for natural scenes. FA Wichmann, LT Sharpe, KR Gegenfurtner Journal of Experimental Psychology: Learning, Memory, and Cognition 28 (3), 509, 2002 | 412 | 2002 |
| Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data HH Schütt, S Harmeling, JH Macke, FA Wichmann Vision research 122, 105-123, 2016 | 383 | 2016 |
| 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 | 316 | 2017 |
| Inference for psychometric functions in the presence of nonstationary behavior I Fründ, NV Haenel, FA Wichmann Journal of vision 11 (6), 16-16, 2011 | 306 | 2011 |
| A nonparametric approach to bottom-up visual saliency W Kienzle, FA Wichmann, M Franz, B Schölkopf Advances in neural information processing systems 19, 2006 | 268 | 2006 |
| Partial success in closing the gap between human and machine vision R Geirhos, K Narayanappa, B Mitzkus, T Thieringer, M Bethge, ... Advances in Neural Information Processing Systems 34, 23885-23899, 2021 | 250 | 2021 |
| Quantifying the effect of intertrial dependence on perceptual decisions I Fründ, FA Wichmann, JH Macke Journal of vision 14 (7), 9-9, 2014 | 214 | 2014 |
| Bayesian inference for psychometric functions M Kuss, F Jäkel, FA Wichmann Journal of Vision 5 (5), 8-8, 2005 | 192 | 2005 |
| Center-surround patterns emerge as optimal predictors for human saccade targets W Kienzle, MO Franz, B Schölkopf, FA Wichmann Journal of vision 9 (5), 7-7, 2009 | 188 | 2009 |
| Spatial four-alternative forced-choice method is the preferred psychophysical method for naïve observers F Jäkel, FA Wichmann Journal of vision 6 (11), 13-13, 2006 | 169 | 2006 |
| Texture and haptic cues in slant discrimination: reliability-based cue weighting without statistically optimal cue combination P Rosas, J Wagemans, MO Ernst, FA Wichmann JOSA A 22 (5), 801-809, 2005 | 138 | 2005 |
| Animal detection in natural scenes: Critical features revisited FA Wichmann, J Drewes, P Rosas, KR Gegenfurtner Journal of Vision 10 (4), 6-6, 2010 | 133 | 2010 |
| 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, 13890-13902, 2020 | 130 | 2020 |
| Phase noise and the classification of natural images FA Wichmann, DI Braun, KR Gegenfurtner Vision research 46 (8-9), 1520-1529, 2006 | 118 | 2006 |
| Spatial statistics and attentional dynamics in scene viewing R Engbert, HA Trukenbrod, S Barthelmé, FA Wichmann Journal of vision 15 (1), 14-14, 2015 | 111 | 2015 |
Matthias BethgeTübingen University & Maddox Co-FounderVerified email at bethgelab.org
