Supervised learning aims to train a classifier under the assumption that training and test
data are from the same distribution. To ease the above assumption, researchers have …

Abstract Out-of-distribution (OOD) detection discerns OOD data where the predictor cannot
make valid predictions as in-distribution (ID) data, thereby increasing the reliability of open …

Abstract Out-of-distribution (OOD) detection is important for deploying machine learning
models in the real world, where test data from shifted distributions can naturally arise. While …

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 …

Unsupervised out-of-distribution (OOD) detection is essential for the reliability of machine
learning. In the literature, existing work has shown that higher-level semantics captured by …

Existing semi-supervised learning (SSL) studies typically assume that unlabeled and test
data are drawn from the same distribution as labeled data. However, in many real-world …

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 …

Modern neural networks can assign high confidence to inputs drawn from outside the
training distribution, posing threats to models in real-world deployments. While much …

By design, discriminatively trained neural network classifiers produce reliable predictions
only for in-distribution samples. For their real-world deployments, detecting out-of …

The task of out-of-distribution (OOD) detection is crucial for deploying machine learning
models in real-world settings. In this paper, we observe that the singular value distributions …