Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

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 …

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

Reliable detection of out-of-distribution (OOD) inputs is increasingly understood to be a
precondition for deployment of machine learning systems. This paper proposes and …

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) data is crucial for robust machine learning systems.
Normalizing flows are flexible deep generative models that often surprisingly fail to …

Open set recognition enables deep neural networks (DNNs) to identify samples of unknown
classes, while maintaining high classification accuracy on samples of known classes …

Abstract Out-of-distribution (OOD) detection is a critical task for ensuring the reliability and
safety of deep neural networks in real-world scenarios. Different from most previous OOD …

Most anomaly detection (AD) models are learned using only normal samples in an
unsupervised way, which may result in ambiguous decision boundary and insufficient …

Deep semi-supervised learning (SSL) methods aim to take advantage of abundant
unlabeled data to improve the algorithm performance. In this paper, we consider the …