| Proximal backpropagation T Frerix, T Möllenhoff, M Moeller, D Cremers International Conference on Learning Representations (ICLR), 2017 | 134 | 2017 |
| The primal-dual hybrid gradient method for semiconvex splittings T Möllenhoff, E Strekalovskiy, M Moeller, D Cremers SIAM Journal on Imaging Sciences 8 (2), 827-857, 2015 | 54 | 2015 |
| Sublabel-accurate relaxation of nonconvex energies T Möllenhoff, E Laude, M Moeller, J Lellmann, D Cremers Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 47 | 2016 |
| Fight ill-posedness with ill-posedness: Single-shot variational depth super-resolution from shading B Haefner, Y Quéau, T Möllenhoff, D Cremers Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 44 | 2018 |
| Low rank priors for color image regularization T Möllenhoff, E Strekalovskiy, M Möller, D Cremers Energy Minimization Methods in Computer Vision and Pattern Recognition: 10th …, 2015 | 27 | 2015 |
| Sublabel-accurate convex relaxation of vectorial multilabel energies E Laude, T Möllenhoff, M Moeller, J Lellmann, D Cremers Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 25 | 2016 |
| Sublabel-accurate discretization of nonconvex free-discontinuity problems T Möllenhoff, D Cremers Proceedings of the IEEE International Conference on Computer Vision, 1183-1191, 2017 | 18 | 2017 |
| Controlling neural networks via energy dissipation M Moeller, T Mollenhoff, D Cremers Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 17 | 2019 |
| Lifting vectorial variational problems: a natural formulation based on geometric measure theory and discrete exterior calculus T Mollenhoff, D Cremers Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 12 | 2019 |
| Combinatorial preconditioners for proximal algorithms on graphs T Möllenhoff, Z Ye, T Wu, D Cremers International Conference on Artificial Intelligence and Statistics, 38-47, 2018 | 6 | 2018 |
| Efficient convex optimization for minimal partition problems with volume constraints T Möllenhoff, C Nieuwenhuis, E Töppe, D Cremers Energy Minimization Methods in Computer Vision and Pattern Recognition: 9th …, 2013 | 5 | 2013 |
| Lifting the convex conjugate in Lagrangian relaxations: a tractable approach for continuous Markov random fields H Bauermeister, E Laude, T Möllenhoff, M Moeller, D Cremers SIAM Journal on Imaging Sciences 15 (3), 1253-1281, 2022 | 4 | 2022 |
| Flat Metric Minimization with Applications in Generative Modeling T Möllenhoff, D Cremers International Conference on Machine Learning (ICML), 4626--4635, 2019 | 3 | 2019 |
| Sublabel-accurate multilabeling meets product label spaces Z Ye, B Haefner, Y Quéau, T Möllenhoff, D Cremers Pattern Recognition: 43rd DAGM German Conference, DAGM GCPR 2021, Bonn …, 2022 | 2 | 2022 |
| Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning Z Ye, T Möllenhoff, T Wu, D Cremers International Conference on Artificial Intelligence and Statistics, 657-668, 2020 | 2 | 2020 |
| SAM as an Optimal Relaxation of Bayes T Möllenhoff, ME Khan arXiv preprint arXiv:2210.01620, 2022 | 1 | 2022 |
| The Lie-Group Bayesian Learning Rule EM Kıral, T Möllenhoff, ME Khan arXiv preprint arXiv:2303.04397, 2023 | | 2023 |
| A Cutting-Plane Method for Sublabel-Accurate Relaxation of Problems with Product Label Spaces Z Ye, B Haefner, Y Quéau, T Möllenhoff, D Cremers International Journal of Computer Vision 131 (1), 346-362, 2023 | | 2023 |
| Non-Smooth Energy Dissipating Networks H Dröge, T Möllenhoff, M Möller 2022 IEEE International Conference on Image Processing (ICIP), 3281-3285, 2022 | | 2022 |
| Efficient Lifting Methods for Variational Problems T Möllenhoff Technische Universität München, 2020 | | 2020 |
Daniel CremersTechnical University of MunichVerified email at tum.de
