6 papers · 1 filter
Towards Fast and Disentangled Counterfactuals for Visual Foundation Models
Sidney Bender, Benedikt Kunz, Ahmed Zeid +3
Foundation models remain vulnerable to spurious correlations and ``Clever Hans'' strategies. Explainable machine learning can find and remove such strategies for classifiers withou…
Reproducibility study on how to find Spurious Correlations, Shortcut Learning, Clever Hans or Group-Distributional non-robustness and how to fix them
Ole Delzer, Sidney Bender
Deep Neural Networks (DNNs) are increasingly utilized in high-stakes domains like medical diagnostics and autonomous driving where model reliability is critical. However, the resea…
SCE-LITE-HQ: Smooth visual counterfactual explanations with generative foundation models
Ahmed Zeid, Sidney Bender
Modern neural networks achieve strong performance but remain difficult to interpret in high-dimensional visual domains. Counterfactual explanations (CFEs) provide a principled appr…
Protein Counterfactuals via Diffusion-Guided Latent Optimization
Weronika Kłos, Sidney Bender, Lukas Kades
Deep learning models can predict protein properties with unprecedented accuracy but rarely offer mechanistic insight or actionable guidance for engineering improved variants. When…
Visual Disentangled Diffusion Autoencoders: Scalable Counterfactual Generation for Foundation Models
Sidney Bender, Marco Morik
Foundation models, despite their robust zero-shot capabilities, remain vulnerable to spurious correlations and 'Clever Hans' strategies. Existing mitigation methods often rely on u…
An Analysis of Human Alignment of Latent Diffusion Models
Lorenz Linhardt, Marco Morik, Sidney Bender +1
Diffusion models, trained on large amounts of data, showed remarkable performance for image synthesis. They have high error consistency with humans and low texture bias when used f…