Michael Moeller
Michael Moeller
Professor for Computer Vision, University of Siegen, Germany
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Zitiert von
Zitiert von
Inverting gradients-how easy is it to break privacy in federated learning?
J Geiping, H Bauermeister, H Dröge, M Moeller
Advances in neural information processing systems 33, 16937-16947, 2020
An adaptive IHS pan-sharpening method
S Rahmani, M Strait, D Merkurjev, M Moeller, T Wittman
IEEE Geoscience and Remote Sensing Letters 7 (4), 746-750, 2010
Learning proximal operators: Using denoising networks for regularizing inverse imaging problems
T Meinhardt, M Moller, C Hazirbas, D Cremers
Proceedings of the IEEE International Conference on Computer Vision, 1781-1790, 2017
A convex model for nonnegative matrix factorization and dimensionality reduction on physical space
E Esser, M Moller, S Osher, G Sapiro, J Xin
IEEE Transactions on Image Processing 21 (7), 3239-3252, 2012
Witches' brew: Industrial scale data poisoning via gradient matching
J Geiping, L Fowl, WR Huang, W Czaja, G Taylor, M Moeller, T Goldstein
arXiv preprint arXiv:2009.02276, 2020
Variational depth from focus reconstruction
M Moeller, M Benning, C Schönlieb, D Cremers
IEEE Transactions on Image Processing 24 (12), 5369-5378, 2015
A variational approach for sharpening high dimensional images
M Möller, T Wittman, AL Bertozzi, M Burger
SIAM Journal on Imaging Sciences 5 (1), 150-178, 2012
Spectral decompositions using one-homogeneous functionals
M Burger, G Gilboa, M Moeller, L Eckardt, D Cremers
SIAM Journal on Imaging Sciences 9 (3), 1374-1408, 2016
A variational approach to hyperspectral image fusion
M Moeller, T Wittman, AL Bertozzi
Algorithms and Technologies for Multispectral, Hyperspectral, and …, 2009
Collaborative total variation: A general framework for vectorial TV models
J Duran, M Moeller, C Sbert, D Cremers
SIAM Journal on Imaging Sciences 9 (1), 116-151, 2016
Point-wise map recovery and refinement from functional correspondence
E Rodolà, M Moeller, D Cremers
arXiv preprint arXiv:1506.05603, 2015
The primal-dual hybrid gradient method for semiconvex splittings
T Mollenhoff, E Strekalovskiy, M Moeller, D Cremers
SIAM Journal on Imaging Sciences 8 (2), 827-857, 2015
Stochastic training is not necessary for generalization
J Geiping, M Goldblum, PE Pope, M Moeller, T Goldstein
arXiv preprint arXiv:2109.14119, 2021
What doesn't kill you makes you robust (er): How to adversarially train against data poisoning
J Geiping, L Fowl, G Somepalli, M Goldblum, M Moeller, T Goldstein
arXiv preprint arXiv:2102.13624, 2021
An adaptive inverse scale space method for compressed sensing
M Burger, M Möller, M Benning, S Osher
Mathematics of Computation 82 (281), 269-299, 2013
Sublabel-accurate relaxation of nonconvex energies
T Mollenhoff, E Laude, M Moeller, J Lellmann, D Cremers
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016
Improving deep learning for HAR with shallow LSTMs
M Bock, A Hölzemann, M Moeller, K Van Laerhoven
Proceedings of the 2021 ACM International Symposium on Wearable Computers, 7-12, 2021
Ds*: Tighter lifting-free convex relaxations for quadratic matching problems
F Bernard, C Theobalt, M Moeller
Proceedings of the IEEE conference on computer vision and pattern …, 2018
Proximal backpropagation
T Frerix, T Möllenhoff, M Moeller, D Cremers
arXiv preprint arXiv:1706.04638, 2017
Variational wavelet pan-sharpening
M Moeller, T Wittman, AL Bertozzi
CAM Report, 08-81, 2008
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