Abstract Out-of-distribution (OOD) detection refers to training the model on in-distribution (ID)
dataset to classify if the input images come from unknown classes. Considerable efforts …

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 …

Detecting out-of-distribution (OOD) samples is crucial for ensuring the safety of machine
learning systems and has shaped the field of OOD detection. Meanwhile, several other …

The aim of this work is to explore the potential of pre-trained vision-language models (VLMs)
for universal detection of AI-generated images. We develop a lightweight detection strategy …

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 a critical task for reliable machine learning. Recent
advances in representation learning give rise to distance-based OOD detection, where …

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