19 citations · 21 across the 7 of their papers we have counts for
9 papers
Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence
Svetlana Pavlitska, Hannes Grolig, J. Marius Zöllner
Increasing the model capacity is a known approach to enhance the adversarial robustness of deep learning networks. On the other hand, various model compression techniques, includin…
Self Supervised Clustering of Traffic Scenes using Graph Representations
Maximilian Zipfl, Moritz Jarosch, J. Marius Zöllner
Examining graphs for similarity is a well-known challenge, but one that is mandatory for grouping graphs together. We present a data-driven method to cluster traffic scenes that is…
Fingerprint of a Traffic Scene: an Approach for a Generic and Independent Scene Assessment
Maximilian Zipfl, Barbara Schütt, J. Marius Zöllner +1
A major challenge in the safety assessment of automated vehicles is to ensure that risk for all traffic participants is as low as possible. A concept that is becoming increasingly…
Robotic Control Using Model Based Meta Adaption
Karam Daaboul, Joel Ikels, Marius Zöllner
In machine learning, meta-learning methods aim for fast adaptability to unknown tasks using prior knowledge. Model-based meta-reinforcement learning combines reinforcement learning…
Measuring Overfitting in Convolutional Neural Networks using Adversarial Perturbations and Label Noise
Svetlana Pavlitskaya, Joël Oswald, J. Marius Zöllner
Although numerous methods to reduce the overfitting of convolutional neural networks (CNNs) exist, it is still not clear how to confidently measure the degree of overfitting. A met…
Is Neuron Coverage Needed to Make Person Detection More Robust?
Svetlana Pavlitskaya, Şiyar Yıkmış, J. Marius Zöllner
The growing use of deep neural networks (DNNs) in safety- and security-critical areas like autonomous driving raises the need for their systematic testing. Coverage-guided testing…