13 citations · 20 across the 9 of their papers we have counts for
10 papers
A neural operator view on U-Nets for inverse imaging problems
Alexander Auras, Martin Burger, Samira Kabri +2
Deep neural networks have shown great empirical success in the solution of a wide variety of ill-posed inverse problems in imaging. Yet, very few works have studied their behavior…
Differentiable Sensor Layouts for End-to-End Learning of Task-Specific Camera Parameters
Hendrik Sommerhoff, Shashank Agnihotri, Mohamed Saleh +3
The success of deep learning is frequently described as the ability to train all parameters of a network on a specific application in an end-to-end fashion. Yet, several design cho…
CCuantuMM: Cycle-Consistent Quantum-Hybrid Matching of Multiple Shapes
Harshil Bhatia, Edith Tretschk, Zorah Lähner +4
Jointly matching multiple, non-rigidly deformed 3D shapes is a challenging, -hard problem. A perfect matching is necessarily cycle-consistent: Following the pairwise…
Convergent Data-driven Regularizations for CT Reconstruction
Samira Kabri, Alexander Auras, Danilo Riccio +4
The reconstruction of images from their corresponding noisy Radon transform is a typical example of an ill-posed linear inverse problem as arising in the application of computerize…
QuAnt: Quantum Annealing with Learnt Couplings
Marcel Seelbach Benkner, Maximilian Krahn, Edith Tretschk +3
Modern quantum annealers can find high-quality solutions to combinatorial optimisation objectives given as quadratic unconstrained binary optimisation (QUBO) problems. Unfortunatel…
On Adversarial Robustness of Deep Image Deblurring
Kanchana Vaishnavi Gandikota, Paramanand Chandramouli, Michael Moeller
Recent approaches employ deep learning-based solutions for the recovery of a sharp image from its blurry observation. This paper introduces adversarial attacks against deep learnin…
A Simple Strategy to Provable Invariance via Orbit Mapping
Kanchana Vaishnavi Gandikota, Jonas Geiping, Zorah Lähner +2
Many applications require robustness, or ideally invariance, of neural networks to certain transformations of input data. Most commonly, this requirement is addressed by training d…
Intrinsic Neural Fields: Learning Functions on Manifolds
Lukas Koestler, Daniel Grittner, Michael Moeller +2
Neural fields have gained significant attention in the computer vision community due to their excellent performance in novel view synthesis, geometry reconstruction, and generative…
Is Differentiable Architecture Search truly a One-Shot Method?
Jonas Geiping, Jovita Lukasik, Margret Keuper +1
Differentiable architecture search (DAS) is a widely researched tool for the discovery of novel architectures, due to its promising results for image classification. The main benef…
What Doesn't Kill You Makes You Robust(er): How to Adversarially Train against Data Poisoning
Jonas Geiping, Liam Fowl, Gowthami Somepalli +3
Data poisoning is a threat model in which a malicious actor tampers with training data to manipulate outcomes at inference time. A variety of defenses against this threat model hav…