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 …

As deep learning methods form a critical part in commercially important applications such as
autonomous driving and medical diagnostics, it is important to reliably detect out-of …

Since deep learning models have been implemented in many commercial applications, it is
important to detect out-of-distribution (OOD) inputs correctly to maintain the performance of …

Abstract Out-of-distribution (OOD) detection has been extensively studied in order to
successfully deploy neural networks, in particular, for safety-critical applications. Moreover …

Abstract Out-of-distribution (OOD) detection is essential to prevent anomalous inputs from
causing a model to fail during deployment. While improved OOD detection methods have …

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 during the inference stage is crucial for deploying
neural networks in the real world. Previous methods commonly relied on the output of a …

In the field of out-of-distribution (OOD) detection, a previous method that use auxiliary data
as OOD data has shown promising performance. However, the method provides an equal …

The real-world deployment of Deep Neural Networks (DNNs) in safety-critical applications
such as autonomous vehicles needs to address a variety of DNNs' vulnerabilities, one of …

Various approaches have been proposed for out-of-distribution (OOD) detection by
augmenting models, input examples, training set, and optimization objectives. Deviating …