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

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 …

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 …

We present a novel vision-language prompt learning approach for few-shot out-of-
distribution (OOD) detection. Few-shot OOD detection aims to detect OOD images from …

Out-of-distribution (OOD) detection is the problem of identifying inputs which are unrelated to
the in-distribution task. The OOD detection performance when the in-distribution (ID) is …

Deep neural networks (DNNs) deployed in real-world applications can encounter out-of-
distribution (OOD) data and adversarial examples. These represent distinct forms of …

In machine learning, it is often observed that standard training outputs anomalously high
confidence for both in-distribution (ID) and out-of-distribution (OOD) data. Thus, the ability to …

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 …

Anomaly detection (AD) is a fundamental research problem in machine learning and
computer vision with practical applications in industrial inspection video surveillance and …