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The Clever Hans Effect in Unsupervised Learning
Jacob Kauffmann, Jonas Dippel, Lukas Ruff +3
Unsupervised learning has become an essential building block of AI systems. The representations it produces, e.g. in foundation models, are critical to a wide variety of downstream…
Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation
Leander Weber, Jim Berend, Moritz Weckbecker +4
Gradient-based optimization has been a cornerstone of machine learning that enabled the vast advances of Artificial Intelligence (AI) development over the past decades. However, th…
Explainable AI for Time Series via Virtual Inspection Layers
Johanna Vielhaben, Sebastian Lapuschkin, Grégoire Montavon +1
The field of eXplainable Artificial Intelligence (XAI) has greatly advanced in recent years, but progress has mainly been made in computer vision and natural language processing. F…
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…
Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations
Alexander Binder, Leander Weber, Sebastian Lapuschkin +3
While the evaluation of explanations is an important step towards trustworthy models, it needs to be done carefully, and the employed metrics need to be well-understood. Specifical…
Data Models for Dataset Drift Controls in Machine Learning With Optical Images
Luis Oala, Marco Aversa, Gabriel Nobis +10
Camera images are ubiquitous in machine learning research. They also play a central role in the delivery of important services spanning medicine and environmental surveying. Howeve…