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Felix A. Wichmann

Felix A. Wichmann

D-Index & Metrics

Engineering and Technology

D-Index
42
Citations
11974
World Ranking
6377
National Ranking
210

Overview

Felix A. Wichmann is affiliated with the University of Tübingen in Germany. Their research primarily spans the fields of Neuroscience and Computer Science, with a specific focus on Cognitive Neuroscience and Artificial Intelligence. Their work explores topics related to visual perception and processing mechanisms, face recognition and perception, adversarial robustness in machine learning, neural dynamics and brain function, color science and applications, domain adaptation, and few-shot learning.

Wichmann's recent publications illustrate an engagement with understanding the behavioral and computational aspects of human and machine vision. Notable papers include:

  • "Are Deep Neural Networks Adequate Behavioral Models of Human Visual Perception?" (2023), Annual Review of Vision Science
  • "The developmental trajectory of object recognition robustness: Children are like small adults but unlike big deep neural networks" (2023), Journal of Vision
  • "Partial success in closing the gap between human and machine vision" (2021), arXiv (Cornell University)
  • "Shortcut learning in deep neural networks" (2020), Nature Machine Intelligence
  • "Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency" (2020), arXiv (Cornell University)

The venues where Wichmann has frequently published research include:

  • Journal of Vision
  • arXiv (Cornell University)
  • Scientific Reports
  • Nature Machine Intelligence
  • Annual Review of Vision Science

Among Wichmann's frequent co-authors are Robert Geirhos, Wieland Brendel, David-Elias Künstle, Kristof Meding, and Matthias Bethge. These collaborations have contributed to a significant body of work, particularly in understanding neural networks and human visual perception.

Wichmann's research integrates aspects of visual cognition with computational modeling, aiming to refine the understanding of how humans and artificial systems perceive and process visual information. The work on adversarial robustness addresses challenges in machine learning, focusing on how neural networks can be vulnerable or resilient to manipulation. Likewise, research in neural dynamics and brain function complements the investigation of perceptual mechanisms at a cognitive and neural level.

Best Publications

  • The psychometric function: I. Fitting, sampling, and goodness of fit

    Felix A. Wichmann;N. Jeremy Hill

  • Shortcut learning in deep neural networks

    Robert Geirhos;Jörn-Henrik Jacobsen;Claudio Michaelis;Richard S. Zemel

  • ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

    Robert Geirhos;Patricia Rubisch;Claudio Michaelis;Matthias Bethge

  • Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data

    Heiko H. Schütt;Heiko H. Schütt;Stefan Harmeling;Jakob H. Macke;Jakob H. Macke;Felix A. Wichmann

  • Generalisation in humans and deep neural networks

    Robert Geirhos;Carlos R. Medina Temme;Jonas Rauber;Heiko H. Schütt

  • The contributions of color to recognition memory for natural scenes

    Felix A. Wichmann;Lindsay T. Sharpe;Karl R. Gegenfurtner

  • Inference for psychometric functions in the presence of nonstationary behavior

    Ingo Fründ;N Valentin Haenel;Felix A Wichmann

  • A Nonparametric Approach to Bottom-Up Visual Saliency

    Wolf Kienzle;Felix A. Wichmann;Matthias O. Franz;Bernhard Schölkopf

  • Quantifying the effect of intertrial dependence on perceptual decisions.

    Ingo Fründ;Ingo Fründ;Felix A. Wichmann;Jakob H. Macke

  • Comparing deep neural networks against humans: object recognition when the signal gets weaker

    Robert Geirhos;David H. J. Janssen;Heiko H. Schütt;Jonas Rauber

  • Center-surround patterns emerge as optimal predictors for human saccade targets.

    Wolf Kienzle;Matthias O. Franz;Bernhard Schölkopf;Felix A. Wichmann

  • Bayesian inference for psychometric functions

    Malte Kuss;Frank Jäkel;Felix A. Wichmann

  • Spatial four-alternative forced-choice method is the preferred psychophysical method for naïve observers.

    Frank Jäkel;Felix A Wichmann

  • Texture and object motion in slant discrimination: Failure of reliability-based weighting of cues may be evidence for strong fusion

    Pedro Rosas;Felix A. Wichmann;Johan Wagemans

  • Texture and haptic cues in slant discrimination: reliability-based cue weighting without statistically optimal cue combination

    Pedro Rosas;Johan Wagemans;Marc O. Ernst;Felix A. Wichmann

  • Animal detection in natural scenes: Critical features revisited

    Felix A. Wichmann;Jan Drewes;Pedro Rosas;Karl R. Gegenfurtner

  • Spatial statistics and attentional dynamics in scene viewing

    Ralf Engbert;Hans A. Trukenbrod;Simon Barthelme;Felix A. Wichmann

  • Partial success in closing the gap between human and machine vision

    Robert Geirhos;Kantharaju Narayanappa;Benjamin Mitzkus;Tizian Thieringer

  • Phase noise and the classification of natural images

    Felix A. Wichmann;Doris I. Braun;Karl R. Gegenfurtner

  • Transcranial magnetic stimulation in the visual system: I. The psychophysics of visual suppression

    Thomas Kammer;Thomas Kammer;Klaas Puls;Hans Strasburger;N. Jeremy Hill

  • Gender Classification of Human Faces

    Arnulf B. A. Graf;Felix A. Wichmann

  • Noise masking of White's illusion exposes the weakness of current spatial filtering models of lightness perception.

    Torsten Betz;Robert Shapley;Felix A. Wichmann;Felix A. Wichmann;Marianne Maertens

Frequent Co-Authors

Matthias Bethge
Matthias Bethge University of Tübingen
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Karl R. Gegenfurtner
Karl R. Gegenfurtner University of Giessen
Heinrich H. Bülthoff
Heinrich H. Bülthoff Max Planck Institute for Biological Cybernetics
Rolf Ulrich
Rolf Ulrich University of Tübingen
Hanspeter A. Mallot
Hanspeter A. Mallot University of Tübingen
Jakob H. Macke
Jakob H. Macke Max Planck Institute for Intelligent Systems
Marc O. Ernst
Marc O. Ernst University of Ulm
Ulrike von Luxburg
Ulrike von Luxburg University of Tübingen

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