220 citations · 333 across the 8 of their papers we have counts for
10 papers
Does enhanced shape bias improve neural network robustness to common corruptions?
Chaithanya Kumar Mummadi, Ranjitha Subramaniam, Robin Hutmacher +3
Convolutional neural networks (CNNs) learn to extract representations of complex features, such as object shapes and textures to solve image recognition tasks. Recent work indicate…
Group Pruning using a Bounded-Lp norm for Group Gating and Regularization
Chaithanya Kumar Mummadi, Tim Genewein, Dan Zhang +2
Deep neural networks achieve state-of-the-art results on several tasks while increasing in complexity. It has been shown that neural networks can be pruned during training by impos…
Grid Saliency for Context Explanations of Semantic Segmentation
Lukas Hoyer, Mauricio Munoz, Prateek Katiyar +2
Recently, there has been a growing interest in developing saliency methods that provide visual explanations of network predictions. Still, the usability of existing methods is limi…
Short-Term Prediction and Multi-Camera Fusion on Semantic Grids
Lukas Hoyer, Patrick Kesper, Anna Khoreva +1
An environment representation (ER) is a substantial part of every autonomous system. It introduces a common interface between perception and other system components, such as decisi…
Functionally Modular and Interpretable Temporal Filtering for Robust Segmentation
Jörg Wagner, Volker Fischer, Michael Herman +1
The performance of autonomous systems heavily relies on their ability to generate a robust representation of the environment. Deep neural networks have greatly improved vision-base…
Hierarchical Recurrent Filtering for Fully Convolutional DenseNets
Jörg Wagner, Volker Fischer, Michael Herman +1
Generating a robust representation of the environment is a crucial ability of learning agents. Deep learning based methods have greatly improved perception systems but still fail i…
The streaming rollout of deep networks - towards fully model-parallel execution
Volker Fischer, Jan Köhler, Thomas Pfeil
Deep neural networks, and in particular recurrent networks, are promising candidates to control autonomous agents that interact in real-time with the physical world. However, this…
Universal Adversarial Perturbations Against Semantic Image Segmentation
Jan Hendrik Metzen, Mummadi Chaithanya Kumar, Thomas Brox +1
While deep learning is remarkably successful on perceptual tasks, it was also shown to be vulnerable to adversarial perturbations of the input. These perturbations denote noise add…
Adversarial Examples for Semantic Image Segmentation
Volker Fischer, Mummadi Chaithanya Kumar, Jan Hendrik Metzen +1
Machine learning methods in general and Deep Neural Networks in particular have shown to be vulnerable to adversarial perturbations. So far this phenomenon has mainly been studied…
On Detecting Adversarial Perturbations
Jan Hendrik Metzen, Tim Genewein, Volker Fischer +1
Machine learning and deep learning in particular has advanced tremendously on perceptual tasks in recent years. However, it remains vulnerable against adversarial perturbations of…