2 papers
cs.AI2026
CORE: Robust Out-of-Distribution Detection via Confidence and Orthogonal Residual Scoring
Jin Mo Yang, Hyung-Sin Kim, Saewoong Bahk
Out-of-distribution (OOD) detection is essential for deploying deep learning models reliably, yet no single method performs consistently across architectures and datasets -- a scor…
cs.CV2026
Tracking the Discriminative Axis: Dual Prototypes for Test-Time OOD Detection Under Covariate Shift
Wooseok Lee, Jin Mo Yang, Saewoong Bahk +1
For reliable deployment of deep-learning systems, out-of-distribution (OOD) detection is indispensable. In the real world, where test-time inputs often arrive as streaming mixtures…