M
Matthias Bethge
Researcher at University of Tübingen
Publications - 323
Citations - 31627
Matthias Bethge is an academic researcher from University of Tübingen. The author has contributed to research in topics: Population & Medicine. The author has an hindex of 65, co-authored 279 publications receiving 23123 citations. Previous affiliations of Matthias Bethge include Hannover Medical School & Amazon.com.
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Proceedings ArticleDOI
Image Style Transfer Using Convolutional Neural Networks
TL;DR: A Neural Algorithm of Artistic Style is introduced that can separate and recombine the image content and style of natural images and provide new insights into the deep image representations learned by Convolutional Neural Networks and demonstrate their potential for high level image synthesis and manipulation.
Journal ArticleDOI
DeepLabCut: markerless pose estimation of user-defined body parts with deep learning
Alexander Mathis,Pranav Mamidanna,Kevin M. Cury,Taiga Abe,Venkatesh N. Murthy,Mackenzie W. Mathis,Mackenzie W. Mathis,Matthias Bethge +7 more
TL;DR: Using a deep learning approach to track user-defined body parts during various behaviors across multiple species, the authors show that their toolbox, called DeepLabCut, can achieve human accuracy with only a few hundred frames of training data.
Posted Content
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos,Patricia Rubisch,Claudio Michaelis,Matthias Bethge,Felix A. Wichmann,Wieland Brendel +5 more
TL;DR: It is shown that ImageNet-trained CNNs are strongly biased towards recognising textures rather than shapes, which is in stark contrast to human behavioural evidence and reveals fundamentally different classification strategies.
Proceedings Article
Texture synthesis using convolutional neural networks
TL;DR: A new model of natural textures based on the feature spaces of convolutional neural networks optimised for object recognition is introduced, showing that across layers the texture representations increasingly capture the statistical properties of natural images while making object information more and more explicit.
Posted Content
A Neural Algorithm of Artistic Style
TL;DR: This work introduces an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality and offers a path forward to an algorithmic understanding of how humans create and perceive artistic imagery.