4 citations · 9 across the 4 of their papers we have counts for
5 papers
Perceived face similarity reflects inverse-generative and naturalistic discriminative objectives
Wenxuan Guo, Heiko H. Schütt, Kamila Maria Jozwik +3
The perceptual representations supporting our ability to recognize faces remain a computational mystery. Deep neural networks offer mechanistic hypotheses for human face perception…
HEM: a margin-based loss for visual categorisation tasks
Michael W. Spratling, Heiko H. Schütt
Training deep neural networks (DNNs) on classification tasks can be performed with a number of different losses, but cross-entropy (CE) loss is the de-facto standard. Here, we prop…
Distinguishing representational geometries with controversial stimuli: Bayesian experimental design and its application to face dissimilarity judgments
Tal Golan, Wenxuan Guo, Heiko H. Schütt +1
Comparing representations of complex stimuli in neural network layers to human brain representations or behavioral judgments can guide model development. However, even qualitativel…
Deep Neural Models for color discrimination and color constancy
Alban Flachot, Arash Akbarinia, Heiko H. Schütt +3
Color constancy is our ability to perceive constant colors across varying illuminations. Here, we trained deep neural networks to be color constant and evaluated their performance…
Disentangling top-down vs. bottom-up and low-level vs. high-level influences on eye movements over time
Heiko H. Schütt, Lars O. M. Rothkegel, Hans A. Trukenbrod +2
Bottom-up and top-down, as well as low-level and high-level factors influence where we fixate when viewing natural scenes. However, the importance of each of these factors and how…