2019


W. Brendel and M. Bethge
Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet
International Conference on Learning Representations (ICLR), 2019
BibTex
R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
International Conference on Learning Representations (ICLR), 2019
Code, URL, BibTex
A. S. Ecker, F. H. Sinz, E. Froudarakis, P. G. Fahey, S. A. Cadena, E. Y. Walker, E. Cobos, J. Reimer, et al.
A rotation-equivariant convolutional neural network model of primary visual cortex
International Conference on Learning Representations (ICLR), 2019
#v1, #system identification, #microns, #convolutional neural network, #rotation equivariance
URL, PDF, BibTex
L. Schott, J. Rauber, W. Brendel, and M. Bethge
Towards the first adversarially robust neural network model on MNIST
International Conference on Learning Representations (ICLR), 2019
URL, BibTex
J.-H. Jacobsen, J. Behrmann, R. Zemel, and M. Bethge
Excessive Invariance Causes Adversarial Vulnerability
International Conference on Learning Representations (ICLR), 2019
BibTex
University of Tuebingen BCCN CIN MPI