118 citations · 219 across the 22 of their papers we have counts for
8 papers · 1 filter
Explainable AI in Grassland Monitoring: Enhancing Model Performance and Domain Adaptability
Shanghua Liu, Anna Hedström, Deepak Hanike Basavegowda +2
Grasslands are known for their high biodiversity and ability to provide multiple ecosystem services. Challenges in automating the identification of indicator plants are key obstacl…
Prototypical Self-Explainable Models Without Re-training
Srishti Gautam, Ahcene Boubekki, Marina M. C. Höhne +1
Explainable AI (XAI) has unfolded in two distinct research directions with, on the one hand, post-hoc methods that explain the predictions of a pre-trained black-box model and, on…
Labeling Neural Representations with Inverse Recognition
Kirill Bykov, Laura Kopf, Shinichi Nakajima +2
Deep Neural Networks (DNNs) demonstrate remarkable capabilities in learning complex hierarchical data representations, but the nature of these representations remains largely unkno…
Kidnapping Deep Learning-based Multirotors using Optimized Flying Adversarial Patches
Pia Hanfeld, Khaled Wahba, Marina M. -C. Höhne +2
Autonomous flying robots, such as multirotors, often rely on deep learning models that make predictions based on a camera image, e.g. for pose estimation. These models can predict…
Flying Adversarial Patches: Manipulating the Behavior of Deep Learning-based Autonomous Multirotors
Pia Hanfeld, Marina M. -C. Höhne, Michael Bussmann +1
Autonomous flying robots, e.g. multirotors, often rely on a neural network that makes predictions based on a camera image. These deep learning (DL) models can compute surprising re…
Mark My Words: Dangers of Watermarked Images in ImageNet
Kirill Bykov, Klaus-Robert Müller, Marina M. -C. Höhne
The utilization of pre-trained networks, especially those trained on ImageNet, has become a common practice in Computer Vision. However, prior research has indicated that a signifi…
Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science
Philine Bommer, Marlene Kretschmer, Anna Hedström +2
Explainable artificial intelligence (XAI) methods shed light on the predictions of machine learning algorithms. Several different approaches exist and have already been applied in…
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus
Anna Hedström, Philine Bommer, Kristoffer K. Wickstrøm +3
One of the unsolved challenges in the field of Explainable AI (XAI) is determining how to most reliably estimate the quality of an explanation method in the absence of ground truth…