Publications by Matthias Bethge

Data for Contrasting action and posture coding with hierarchical deep neural network models of proprioception

Mackenzie Mathis, Alexander Mathis, Matthias Bethge

############# Contrasting action and posture coding with hierarchical deep neural network models of proprioception, eLife 2023 ############# Authors: Kai J Sandbrink, Pranav Mamidanna, Claudio Michaelis, Matthias Bethge, Mackenzie W Mathis and Alexander ...

Zenodo2024

Contrasting action and posture coding with hierarchical deep neural network models of proprioception

Pranav Mamidanna, Mackenzie Mathis, Alexander Mathis, Kai Jappe Sandbrink, Matthias Bethge

Biological motor control is versatile, efficient, and depends on proprioceptive feedback. Muscles are flexible and undergo continuous changes, requiring distributed adaptive control mechanisms that continuously account for the body's state. The canonical r ...

eLIFE SCIENCES PUBL LTD2023

Fixing the problems of deep neural networks will require better training data and learning algorithms

Adrien Christophe Doerig, Jianghao Liu, Kuntal Ghosh, Matthias Bethge, Martin Schrimpf

Bowers et al. argue that deep neural networks (DNNs) are poor models of biological vision because they often learn to rival human accuracy by relying on strategies that differ markedly from those of humans. We show that this problem is worsening as DNNs ar ...

Cambridge Univ Press2023

Let's move forward: Image-computable models and a common model evaluation scheme are prerequisites for a scientific understanding of human vision

Matthias Bethge, Martin Schrimpf

In the target article, Bowers et al. dispute deep artificial neural network (ANN) models as the currently leading models of human vision without producing alternatives. They eschew the use of public benchmarking platforms to compare vision models with the ...

Cambridge Univ Press2023

Pretraining boosts out-of-domain robustness for pose estimation

Mackenzie Mathis, Alexander Mathis, Steffen Schneider, Thomas Ray Biasi, Mert Yüksekgönül, Matthias Bethge

Neural networks are highly effective tools for pose estimation. However, as in other computer vision tasks, robustness to out-of-domain data remains a challenge, especially for small training sets that are common for real-world applications. Here, we probe ...

IEEE COMPUTER SOC2021

Gravitational Lensing Accuracy Testing 2010 (Great10) Challenge Handbook

Frédéric Courbin, Marc Gentile, Guldariya Nurbaeva, Anaïs Marie Rassat, Richard Massey, Michael Hirsch, Matthias Bethge

GRavitational lEnsing Accuracy Testing 2010 (GREAT10) is a public image analysis challenge aimed at the development of algorithms to analyze astronomical images. Specifically, the challenge is to measure varying image distortions in the presence of a varia ...

2011

Results of the GREAT08 Challenge: an image analysis competition for cosmological lensing

Frédéric Courbin, Marc Gentile, Anaïs Marie Rassat, Richard Massey, Michael Hirsch, Matthias Bethge

We present the results of the Gravitational LEnsing Accuracy Testing 2008 (GREAT08) Challenge, a blind analysis challenge to infer weak gravitational lensing shear distortions from images. The primary goal was to stimulate new ideas by presenting the probl ...

Blackwell Publishing Ltd, Oxford2010