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 …

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 …

Reliable uncertainty from deterministic single-forward pass models is sought after because
conventional methods of uncertainty quantification are computationally expensive. We take …

Abstract Out-of-distribution (OOD) detection is a critical task for deploying machine learning
models in the open world. Distance-based methods have demonstrated promise, where …

Abstract Machine-learning models for medical tasks can match or surpass the performance
of clinical experts. However, in settings differing from those of the training dataset, the …

Recognizing out-of-distribution (OOD) samples is critical for machine learning systems
deployed in the open world. The vast majority of OOD detection methods are driven by a …

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 …

Near out-of-distribution detection (OOD) is a major challenge for deep neural networks. We
demonstrate that large-scale pre-trained transformers can significantly improve the state-of …

Novelty detection, ie, identifying whether a given sample is drawn from outside the training
distribution, is essential for reliable machine learning. To this end, there have been many …

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 …