arXiv
2024
Ahlert, J., Klein, T., Wichmann, F. A., and Geirhos, R. (2024). How aligned are different alignment metrics? arXiv:2407.07530, 1-11.
Huber, L. S., Künstle, D.-E., and Reuter, K. (2024). Tracing Truth Through Conceptual Scaling: Mapping People’s Understanding of Abstract Concepts. PsyArXiv Preprints:c42yr, 1-8.
Huber, L. S., Mast, F. W., and Wichmann, F. A. (2024). Immediate generalisation in humans but a generalisation lag in deep neural networks—evidence for representational divergence? arXiv:2402.09303v2, 1-15.
2023
Wichmann, F. A., and Geirhos, R. (2023). Are Deep Neural Networks Adequate Behavioral Models of Human Visual Perception? arXiv:2305.17023v1, 1-23.
Wichmann, F. A., Kornblith, S., and Geirhos, R. (2023). Neither hype nor gloom do DNNs justice. arXiv:2312.05355v1, 1-3.
2022
Huber, L. S., Geirhos, R., and Wichmann, F. A. (2022). The developmental trajectory of object recognition robustness: children are like small adults but unlike big deep neural networks. arXiv:2205.10144v1, 1-32.
2021
Geirhos, R., Narayanappa, K., Mitzkus, B., Thieringer, T., Bethge, M., Wichmann, F. A., and Brendel, W. (2021). Partial success in closing the gap between human and machine vision. arXiv:2106.07411v1, 1-26.
Meding, K., Schulze Buschoff, L. M., Geirhos, R., and Wichmann, F. A. (2021). Trivial or impossible -- dichotomous data difficulty masks model differences (on ImageNet and beyond). arXiv:2110.05922v3, 1-38.
2020
Esteve-Taboada, J. J., Aguilar, G., Maertens, M., Wichmann, F. A., and Malo, J. (2020). Psychophysical Estimation of Early and Late Noise. arXiv:2012.06608v1, 1-15.
Flachot, A., Akbarinia, A., Schütt, H. H., Fleming, R. W., Wichmann, F. A., and Gegenfurtner, K. R. (2020). Deep Neural Models for color discrimination and color constancy. arXiv:2012.14402v1, 1-19.
Geirhos, R., Narayanappa, K., Mitzkus, B., Bethge, M., Wichmann, F. A., and Brendel, W. (2020). On the surprising similarities between supervised and self-supervised models. arXiv:2010.08377v1, 1-10.
Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R., Brendel, W., Bethge M. and Wichmann, F. A. (2020). Shortcut Learning in Deep Neural Networks. arXiv:2004.07780v1, 1-27.
Geirhos, R., Meding, K. and Wichmann, F. A. (2020). Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency. arXiv:2006.16736v1, 1-21.
Hagendorff, T. and Meding, K. (2020). The Big Picture: Ethical Considerations and Statistical Analysis of Industry Involvement in Machine Learning Research. arXiv:2006.04541v1, 1-16.
2019
Haghiri, S., Rubisch, P., Geirhos, R., Wichmann, F. A. and von Luxburg, U. (2019). Comparison-Based Framework for Psychophysics: Lab versus Crowdsourcing. arXiv, 1905.07234v2, 1-19.
Michaelis, C., Mitzkus, B., Geirhos, R., Rusak, E., Bringmann, O., Ecker, A.S., Bethge, M. and Brendel, W. (2019). Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming. arXiv, 1907.07484v1, 1-23.
2018
Geirhos, R., Rubisch, P. , Michaelis, C. , Bethge, M., Wichmann, F. A. and Brendel, W. (2018). ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. arXiv, 1811.12231v1 , 1-21.
Geirhos, R., Medina Temme, C. R., Rauber, J., Schütt, H. H., Bethge, M., and Wichmann, F. A. (2018). Generalisation in humans and deep neural networks. arXiv, 1808.08750v1, 1-26.
Rothkegel, L. O. M., Schütt, H. H., Trukenbrod, H. A., Wichmann, F. A., and Engbert, R. (2018). Searchers adjust their eye movement dynamics to the target characteristics in natural scenes. arXiv, 1802.04069v1, 1-12.
Schütt, H. H., Rothkegel, L. O. M., Trukenbrod, H. A., Engbert, R. and Wichmann, F. A. (2018). Disentangling top-down vs. bottom-up and low-level vs. high-level influences on eye movements over time. arXiv, 1803.07352v1, 1-25.
Trukenbrod, H. A., Barthelmé, S., Wichmann, F. A. and Engbert, R. (2018). Spatial statistics for gaze patterns in scene viewing: Effects of repeated viewing. arXiv, 1704.01761v2 , 1-27.
2017
Geirhos, R., Janssen, D. H. J., Schütt, H. H., Rauber, J., Bethge, M. and Wichmann, F. A. (2017). Comparing deep neural networks against humans: object recognition when the signal gets weaker. arXiv, 1706.06969v1, 1-31.
2016
Rothkegel, L. O. M., Trukenbrod, H. A., Schütt, H. H., Wichmann, F. A. and Engbert, R. (2016). The temporal evolution of the central fixation bias in scene viewing. arXiv, 1610.05982v3, 1-43.
Rothkegel, L. O. M., Trukenbrod, H. A., Schütt, H. H., Wichmann, F. A. and Engbert, R. (2016). Influence of initial fixation position in scene viewing. arXiv, 1606.09095v2, 1-34.
Schütt, H. H., Rothkegel, L. O. M., Trukenbrod, H. A., Reich, S., Wichmann, F. A. and Engbert, R. (2016). Likelihood-based Parameter Estimation and Comparison of Dynamical Cognitive Models. arXiv, 1606.07309v2, 1-29.
2014
Engbert, R., Trukenbrod, H. A., Barthelmé, S. and Wichmann, F. A. (2014). Spatial statistics and attentional dynamics in scene viewing. arXiv, 1405.3270v2, 1–29.
2012
Barthelmé, S., Trukenbrod, H. A., Engbert, R., and Wichmann, F. A. (2012). Modelling fixation locations using spatial point processes. arXiv, 1207.2370v3, 1–41.