Neural Information Processing
My laboratory investigates human perception combining psychophysical experiments with computational modelling. Currently we have four research foci: First, to improve our image-based model of early spatial vision. Second, to connect early spatial vision with mid-level vision: perceived lightness, brightness and contrast in relation to surface reflectance and illumination in images of real scenes. Third, we investigate differences and similarities between deep convolutional neural networks and human object recognition. Fourth, we explore connections between causality from a perceptual as well as a machine learning perspective.
- Geirhos, Temme, Rauber, Schütt, Bethge and Wichmann (2018). Generalisation in humans and deep neural networks. Advances in Neural Information Processing Systems (NeurIPS) 31.
- Geirhos, Rubisch, Michaelis, Bethge, Wichmann and Brendel (2019). ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. International Conference on Learning Representations (ICLR), 2019.
- Meding, Janzing, Schölkopf and Wichmann (2019). Perceiving the arrow of time in autoregressive motion. Advances in Neural Information Processing Systems (NeurIPS) 32.
- Schütt and Wichmann (2017). An image-computable psychophysical spatial vision model. Journal of Vision, 17(12):12, 1–35.
- Wichmann and Jäkel (2018). Methods in Psychophysics. Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience, Fourth Edition, Volume 5. Methodology