Utilizing auxiliary outlier datasets to regularize the machine learning model has
demonstrated promise for out-of-distribution (OOD) detection and safe prediction. Due to the …

Machine learning has achieved remarkable success in many applications. However,
existing studies are largely based on the closed-world assumption, which assumes that the …

Out-of-Distribution (OOD) detection is critical for the reliable operation of open-world
intelligent systems. Despite the emergence of an increasing number of OOD detection …

Open-world classification systems should discern out-of-distribution (OOD) data whose
labels deviate from those of in-distribution (ID) cases, motivating recent studies in OOD …

Out-of-distribution (OOD) detection is indispensable for safely deploying machine learning
models in the wild. One of the key challenges is that models lack supervision signals from …

Out-of-distribution (OOD) detection aims to detect “unknown” data whose labels have not
been seen during the in-distribution (ID) training process. Recent progress in representation …

Abstract Out-of-distribution (OOD) detection discerns OOD data where the predictor cannot
make valid predictions as in-distribution (ID) data, thereby increasing the reliability of open …

Detecting out-of-distribution (OOD) samples are crucial for machine learning models
deployed in open-world environments. Classifier-based scores are a standard approach for …

Riemannian diffusion models draw inspiration from standard Euclidean space diffusion
models to learn distributions on general manifolds. Unfortunately, the additional geometric …

A neural network trained on a classification dataset often exhibits a higher vector norm of
hidden layer features for in-distribution (ID) samples, while producing relatively lower norm …