no code implementations • 15 Mar 2024 • Andreas Bär, Neil Houlsby, Mostafa Dehghani, Manoj Kumar
Training a linear classifier or lightweight model on top of pretrained vision model outputs, so-called 'frozen features', leads to impressive performance on a number of downstream few-shot tasks.
1 code implementation • 2 Mar 2022 • Marvin Klingner, Varun Ravi Kumar, Senthil Yogamani, Andreas Bär, Tim Fingscheidt
In this paper, we (i) propose a novel adversarial perturbation detection scheme based on multi-task perception of complex vision tasks (i. e., depth estimation and semantic segmentation).
no code implementations • 29 Apr 2021 • Sebastian Houben, Stephanie Abrecht, Maram Akila, Andreas Bär, Felix Brockherde, Patrick Feifel, Tim Fingscheidt, Sujan Sai Gannamaneni, Seyed Eghbal Ghobadi, Ahmed Hammam, Anselm Haselhoff, Felix Hauser, Christian Heinzemann, Marco Hoffmann, Nikhil Kapoor, Falk Kappel, Marvin Klingner, Jan Kronenberger, Fabian Küppers, Jonas Löhdefink, Michael Mlynarski, Michael Mock, Firas Mualla, Svetlana Pavlitskaya, Maximilian Poretschkin, Alexander Pohl, Varun Ravi-Kumar, Julia Rosenzweig, Matthias Rottmann, Stefan Rüping, Timo Sämann, Jan David Schneider, Elena Schulz, Gesina Schwalbe, Joachim Sicking, Toshika Srivastava, Serin Varghese, Michael Weber, Sebastian Wirkert, Tim Wirtz, Matthias Woehrle
Our paper addresses both machine learning experts and safety engineers: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods.
no code implementations • 12 Apr 2021 • Marvin Klingner, Andreas Bär, Marcel Mross, Tim Fingscheidt
In this work we address the task of observing the performance of a semantic segmentation deep neural network (DNN) during online operation, i. e., during inference, which is of high importance in safety-critical applications such as autonomous driving.
no code implementations • 11 Jan 2021 • Andreas Bär, Jonas Löhdefink, Nikhil Kapoor, Serin J. Varghese, Fabian Hüger, Peter Schlicht, Tim Fingscheidt
Although CNNs obtain state-of-the-art performance on clean images, almost imperceptible changes to the input, referred to as adversarial perturbations, may lead to fatal deception.
no code implementations • 2 Dec 2020 • Nikhil Kapoor, Andreas Bär, Serin Varghese, Jan David Schneider, Fabian Hüger, Peter Schlicht, Tim Fingscheidt
Despite recent advancements, deep neural networks are not robust against adversarial perturbations.
no code implementations • 28 Oct 2020 • Atiye Sadat Hashemi, Andreas Bär, Saeed Mozaffari, Tim Fingscheidt
Using our generated non-targeted UAPs, we obtain an average fooling rate of 93. 36% on the source models (state of the art: 82. 16%).
1 code implementation • 12 May 2020 • Marvin Klingner, Andreas Bär, Philipp Donn, Tim Fingscheidt
While neural networks trained for semantic segmentation are essential for perception in autonomous driving, most current algorithms assume a fixed number of classes, presenting a major limitation when developing new autonomous driving systems with the need of additional classes.
no code implementations • 23 Apr 2020 • Marvin Klingner, Andreas Bär, Tim Fingscheidt
We show the effectiveness of our method on the Cityscapes dataset, where our multi-task training approach consistently outperforms the single-task semantic segmentation baseline in terms of both robustness vs. noise and in terms of adversarial attacks, without the need for depth labels in training.
no code implementations • 25 Feb 2019 • Jan-Aike Bolte, Andreas Bär, Daniel Lipinski, Tim Fingscheidt
The progress in autonomous driving is also due to the increased availability of vast amounts of training data for the underlying machine learning approaches.
no code implementations • 12 Feb 2019 • Jonas Löhdefink, Andreas Bär, Nico M. Schmidt, Fabian Hüger, Peter Schlicht, Tim Fingscheidt
The high amount of sensors required for autonomous driving poses enormous challenges on the capacity of automotive bus systems.