3 papers
cs.CV2026
Unified Multi-Layer Subspace Modeling for Cross-Domain OOD Detection
Gerhard Krumpl, Henning Avenhaus, Horst Possegger
Out-of-Distribution (OOD) detection remains a fundamental challenge for neural networks, whose predictions can be overconfident on inputs that deviate from the training distributio…
cs.CV2026
One Model, Many Behaviors: Training-Induced Effects on Out-of-Distribution Detection
Gerhard Krumpl, Henning Avenhaus, Horst Possegger
Out-of-distribution (OOD) detection is crucial for deploying robust and reliable machine-learning systems in open-world settings. Despite steady advances in OOD detectors, their in…
cs.CV2026
ICONIC-444: A 3.1-Million-Image Dataset for OOD Detection Research
Gerhard Krumpl, Henning Avenhaus, Horst Possegger
Current progress in out-of-distribution (OOD) detection is limited by the lack of large, high-quality datasets with clearly defined OOD categories across varying difficulty levels…