Search Results for author:
Found 7 papers, 2 papers with code
Achieving Efficient and Realistic Full-Radar Simulations and Automatic Data Annotation by exploiting Ray Meta Data of a Radar Ray Tracing Simulator
This approach allows not only almost perfect annotations possible, but also allows the annotation of exotic effects, such as multi-path effects or to label signal parts originating from different parts of an object.
Raising the Bar for Certified Adversarial Robustness with Diffusion Models
In this work, we demonstrate that a similar approach can substantially improve deterministic certified defenses.
FastAMI -- a Monte Carlo Approach to the Adjustment for Chance in Clustering Comparison Metrics
Clustering is at the very core of machine learning, and its applications proliferate with the increasing availability of data.
Just a Matter of Scale? Reevaluating Scale Equivariance in Convolutional Neural Networks
We also demonstrate that our family of models is able to generalize well towards larger scales and improve scale equivariance.
Improving Robustness against Real-World and Worst-Case Distribution Shifts through Decision Region Quantification
The reliability of neural networks is essential for their use in safety-critical applications.
Behind the Machine's Gaze: Neural Networks with Biologically-inspired Constraints Exhibit Human-like Visual Attention
By and large, existing computational models of visual attention tacitly assume perfect vision and full access to the stimulus and thereby deviate from foveated biological vision.
Towards Rapid and Robust Adversarial Training with One-Step Attacks
Further, we add a learnable regularization step prior to the neural network, which we call Pixelwise Noise Injection Layer (PNIL).
