Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Given a long list of anomaly detection algorithms developed in the last few decades, how do
they perform with regard to (i) varying levels of supervision,(ii) different types of anomalies …

Abstract Out-of-distribution (OOD) detection has received much attention lately due to its
practical importance in enhancing the safe deployment of neural networks. One of the …

Unsupervised anomaly detection with localization has many practical applications when
labeling is infeasible and, moreover, when anomaly examples are completely missing in the …

Detecting out-of-distribution (OOD) data has become a critical component in ensuring the
safe deployment of machine learning models in the real world. Existing OOD detection …

Machine learning models often encounter samples that are diverged from the training
distribution. Failure to recognize an out-of-distribution (OOD) sample, and consequently …

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 …

Building reliable object detectors that can detect out-of-distribution (OOD) objects is critical
yet underexplored. One of the key challenges is that models lack supervision signals from …

Deep generative models (DGMs) seem a natural fit for detecting out-of-distribution (OOD)
inputs, but such models have been shown to assign higher probabilities or densities to OOD …

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 …