Abstract Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of
machine learning systems. For instance, in autonomous driving, we would like the driving …

Abstract Out-of-distribution (OOD) detection is a critical task for deploying machine learning
models in the open world. Distance-based methods have demonstrated promise, where …

Abstract Out-of-distribution (OOD) detection is vital to safety-critical machine learning
applications and has thus been extensively studied, with a plethora of methods developed in …

Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying
machine learning models in the real world. Previous methods commonly rely on an OOD …

The goal of continual learning is to improve the performance of recognition models in
learning sequentially arrived data. Although most existing works are established on the …

Abstract Out-of-distribution (OOD) detection is indispensable for machine learning models
deployed in the open world. Recently, the use of an auxiliary outlier dataset during training …

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 …

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