19 citations · 19 across the 4 of their papers we have counts for
3 papers · 1 filter
Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks
Jörg Wagner, Jan Mathias Köhler, Tobias Gindele +3
To verify and validate networks, it is essential to gain insight into their decisions, limitations as well as possible shortcomings of training data. In this work, we propose a pos…
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…