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 …

Detecting out-of-distribution inputs is critical for the safe deployment of machine learning
models in the real world. However, neural networks are known to suffer from the …

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 has received much attention lately due to its
practical importance in enhancing the safe deployment of neural networks. One of the …

Recognizing out-of-distribution (OOD) samples is critical for machine learning systems
deployed in the open world. The vast majority of OOD detection methods are driven by a …

Detecting out-of-distribution (OOD) data has become a critical component in ensuring the
safe deployment of machine learning models in the real world. Existing OOD detection …

Supervised learning aims to train a classifier under the assumption that training and test
data are from the same distribution. To ease the above assumption, researchers have …

Machine learning models often encounter samples that are diverged from the training
distribution. Failure to recognize an out-of-distribution (OOD) sample, and consequently …

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 …

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 …