2017


T. S. A. Wallis, C. M. Funke, A. S. Ecker, L. A. Gatys, F. A. Wichmann, and M. Bethge
A Parametric Texture Model Based on Deep Convolutional Features Closely Matches Texture Appearance for Humans
Journal of Vision, 17(12), 2017
#visual textures, #style transfer, #perceptual image synthesis, #cnns, #psychophysics, #appearance
Code, URL, DOI, Stimuli, Preprint, BibTex
L. A. Gatys, A. S. Ecker, and M. Bethge
Texture and art with deep neural networks
Current Opinion in Neurobiology, 46, 178-186, 2017
#visual textures, #style transfer, #perceptual image synthesis, #cnns, #computational neuroscience
URL, PDF, BibTex
M. Kümmerer, T. S. Wallis, L. A. Gatys, and M. Bethge
Understanding Low- and High-Level Contributions to Fixation Prediction
The IEEE International Conference on Computer Vision (ICCV), 2017
PDF, model webservice, BibTex
D. Klindt, A. S. Ecker, T. Euler, and M. Bethge
Neural system identification for large populations separating “what” and “where”
Advances in Neural Information Processing Systems 30 (in press), 2017
#convolutional neural networks, #system identification, #neural data analysis
BibTex
G. H. Denfield, A. S. Ecker, T. J. Shinn, M. Bethge, and A. S. Tolias
Attentional fluctuations induce shared variability in macaque primary visual cortex
bioRxiv, 2017
#attention, #primary visual cortex, #noise correlations, #internal states
URL, DOI, BibTex
J. Rauber, W. Brendel, and M. Bethge
Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models
Reliable Machine Learning in the Wild Workshop, 34th International Conference on Machine Learning, 2017
Code, URL, BibTex
L. A. Gatys, A. S. Ecker, M. Bethge, A. Hertzmann, and E. Shechtman
Controlling Perceptual Factors in Neural Style Transfer
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017
#texture transfer, #artistic style, #user control, #convolutional neural networks
Code, URL, Supplementary Material, BibTex
M. Kümmerer, T. S. A. Wallis, and M. Bethge
Saliency Benchmarking: Separating Models, Maps and Metrics
arxiv, 2017
URL, BibTex
I. Ustyuzhaninov*, W. Brendel*, L. Gatys, and M. Bethge
What does it take to generate natural textures?
International Conference on Learning Representations, 2017
#deep learning, #texture synthesis, #random networks
Code, URL, PDF, Poster, BibTex
K. Franke, P. Berens, T. Schubert, M. Bethge, T. Euler, and T. Baden
Inhibition decorrelates visual feature representations in the inner retina.
Nature, 542, 439-444, 2017
URL, BibTex
C. M. Funke, L. A. Gatys, A. S. Ecker, and M. Bethge
Synthesising Dynamic Textures using Convolutional Neural Networks
arXiv, 2017
#texture synthesis, #dynamic textures
Code, URL, BibTex
S. A. Cadena, G. H. Denfield, E. Y. Walker, L. A. Gatys, A. S. Tolias, M. Bethge, and A. S. Ecker
Deep convolutional models improve predictions of macaque V1 responses to natural images
bioRxiv, 2017
URL, DOI, BibTex
R. Geirhos, D. H. J. Janssen, H. H. Schütt, J. Rauber, M. Bethge, and F. A. Wichmann
Comparing deep neural networks against humans: object recognition when the signal gets weaker
arXiv, 170606969, 2017
URL, BibTex
T. S. A. Wallis, S. Tobias, M. Bethge, and F. A. Wichmann
Detecting Distortions of Peripherally Presented Letter Stimuli under Crowded Conditions
Attention, Perception & Psychophysics, 2017
#psychophysics, #letters, #spatial distortions, #crowding
URL, DOI, BibTex
University of Tuebingen BCCN CIN MPI