Hannah Dröge

According to our database, Hannah Dröge authored at least 11 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

On the confluence of machine learning and model-based energy minimization methods for computer vision.

Hannah Dröge

PhD thesis, 2024

VHS: High-Resolution Iterative Stereo Matching with Visual Hull Priors.

Markus Plack, Hannah Dröge, Leif Van Holland, Matthias B. Hullin

CoRR, 2024

Robustness and Exploration of Variational and Machine Learning Approaches to Inverse Problems: An Overview.

Alexander Auras, Kanchana Vaishnavi Gandikota, Hannah Dröge, Michael Moeller

CoRR, 2024

2023

Kissing to Find a Match: Efficient Low-Rank Permutation Representation.

Hannah Dröge, Zorah Lähner, Yuval Bahat, Onofre Martorell, Felix Heide, Michael Möller

CoRR, 2023

Kissing to Find a Match: Efficient Low-Rank Permutation Representation.

Hannah Dröge, Zorah Lähner, Yuval Bahat, Onofre Martorell Nadal, Felix Heide, Michael Moeller

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Evaluating Adversarial Robustness of Low dose CT Recovery.

Kanchana Vaishnavi Gandikota, Paramanand Chandramouli, Hannah Dröge, Michael Möller

Proceedings of the Medical Imaging with Deep Learning, 2023

2022

Non-Smooth Energy Dissipating Networks.

Hannah Dröge, Thomas Möllenhoff, Michael Möller

Proceedings of the 2022 IEEE International Conference on Image Processing, 2022

Explorable Data Consistent CT Reconstruction.

Hannah Dröge, Yuval Bahat, Felix Heide, Michael Moeller

Proceedings of the 33rd British Machine Vision Conference 2022, 2022

2021

Mitral Valve Segmentation Using Robust Nonnegative Matrix Factorization.

Hannah Dröge, Baichuan Yuan, Rafael Llerena, Jesse T. Yen, Michael Möller, Andrea L. Bertozzi

J. Imaging, 2021

Learning or Modelling? An Analysis of Single Image Segmentation Based on Scribble Information.

Hannah Dröge, Michael Möller

Proceedings of the 2021 IEEE International Conference on Image Processing, 2021

2020

Inverting Gradients - How easy is it to break privacy in federated learning?

Jonas Geiping, Hartmut Bauermeister, Hannah Dröge, Michael Moeller

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020