13 citations · 20 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
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…