35 citations · 39 across the 2 of their papers we have counts for
4 papers · 1 filter
Towards Robust Explanations for Deep Neural Networks
Ann-Kathrin Dombrowski, Christopher J. Anders, Klaus-Robert Müller +1
Explanation methods shed light on the decision process of black-box classifiers such as deep neural networks. But their usefulness can be compromised because they are susceptible t…
Fairwashing Explanations with Off-Manifold Detergent
Christopher J. Anders, Plamen Pasliev, Ann-Kathrin Dombrowski +2
Explanation methods promise to make black-box classifiers more transparent. As a result, it is hoped that they can act as proof for a sensible, fair and trustworthy decision-making…
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
Wojciech Samek, Grégoire Montavon, Sebastian Lapuschkin +2
With the broader and highly successful usage of machine learning in industry and the sciences, there has been a growing demand for Explainable AI. Interpretability and explanation…
Understanding Patch-Based Learning by Explaining Predictions
Christopher Anders, Grégoire Montavon, Wojciech Samek +1
Deep networks are able to learn highly predictive models of video data. Due to video length, a common strategy is to train them on small video snippets. We apply the deep Taylor /…