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 …

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 …

Using unlabeled data to regularize the machine learning models has demonstrated promise
for improving safety and reliability in detecting out-of-distribution (OOD) data. Harnessing the …

Machine learning models deployed in the wild can be challenged by out-of-distribution
(OOD) data from unknown classes. Recent advances in OOD detection rely on distance …

One of the challenges for neural networks in real-life applications is the overconfident errors
these models make when the data is not from the original training distribution. Addressing …

Abstract Out-of-distribution (OOD) object detection is a challenging task due to the absence
of open-set OOD data. Inspired by recent advancements in text-to-image generative models …

Out-of-distribution (OOD) detection aims to detect test samples outside the training category
space, which is an essential component in building reliable machine learning systems …

Detecting data points deviating from the training distribution is pivotal for ensuring reliable
machine learning. Extensive research has been dedicated to the challenge, spanning …

Semantically coherent out-of-distribution detection (SCOOD) is a recently proposed realistic
OOD detection setting: given labeled in-distribution (ID) data and mixed in-distribution and …

Despite graph neural networks'(GNNs) great success in modelling graph-structured data,
out-of-distribution (OOD) test instances still pose a great challenge for current GNNs. One of …