118 citations · 168 across the 13 of their papers we have counts for
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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…
Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI
Sami Ede, Serop Baghdadlian, Leander Weber +4
The ability to continuously process and retain new information like we do naturally as humans is a feat that is highly sought after when training neural networks. Unfortunately, th…
Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement
Leander Weber, Sebastian Lapuschkin, Alexander Binder +1
Explainable Artificial Intelligence (XAI) is an emerging research field bringing transparency to highly complex and opaque machine learning (ML) models. Despite the development of…
Measurably Stronger Explanation Reliability via Model Canonization
Franz Motzkus, Leander Weber, Sebastian Lapuschkin
While rule-based attribution methods have proven useful for providing local explanations for Deep Neural Networks, explaining modern and more varied network architectures yields ne…
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond
Anna Hedström, Leander Weber, Dilyara Bareeva +5
The evaluation of explanation methods is a research topic that has not yet been explored deeply, however, since explainability is supposed to strengthen trust in artificial intelli…
Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence
Frederik Pahde, Maximilian Dreyer, Leander Weber +5
With a growing interest in understanding neural network prediction strategies, Concept Activation Vectors (CAVs) have emerged as a popular tool for modeling human-understandable co…