Author

Margret Keuper

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Preprint Aug 2026

Failure or Drift? Evaluating Monocular SLAM under Synthetic and Real-World Corruptions

Visual SLAM is commonly evaluated on clean trajectories, although deployment failures are often caused by adverse weather, illumination, blur, and sensor artifacts. Controlled corruptions are attractive because they isolate such factors, but a synthetic stress test is useful only when it leads to the same engineering c...

A. S. Thomas, Shashank Agnihotri, Margret Keuper · 0 citations

#machine learning Preprint Jul 2025

Faithful, Interpretable Chest X-ray Diagnosis with Artifact-free B-cos Networks

This work introduces anti-aliasing strategies using ASAP and BlurPool to significantly improve explanation quality and preserves strong predictive performance while providing faithful and artifact-free explanations suitable for clinical application in multi-class and multi-label settings.

Shreyash Arya, Shashank Agnihotri, Marcel Kleinmann et al. · 0 citations

#machine learning Preprint Aug 2026

RL-FAT: Reinforcement Learning for Fair Adversarial Training

RL-FAT is proposed, a reinforcement-learning-inspired fair adversarial training framework that uses policy-gradient based feedback from adversarial predictions to improve adversarial robustness while promoting a more balanced robustness distribution across classes.

Tejaswini Medi, Levan Mikeladze, Margret Keuper · 0 citations