Abstract Machine learning methods such as deep neural networks (DNNs), despite their
success across different domains, are known to often generate incorrect predictions with …

Abstract Out-of-distribution (OOD) detection and lossless compression constitute two
problems that can be solved by the training of probabilistic models on a first dataset with …

Novelty detection is a fundamental task of machine learning which aims to detect abnormal
(ie out-of-distribution (OOD)) samples. Since diffusion models have recently emerged as the …

Reliable out-of-distribution (OOD) detection is fundamental to implementing safer modern
machine learning (ML) systems. In this paper, we introduce Igeood, an effective method for …

We address the problem of out-of-distribution (OOD) detection for the task of object
detection. We show that residual convolutional layers with batch normalisation produce …

We propose a principled and practical method for out-of-distribution (OoD) detection with
deep hybrid models (DHMs), which model the joint density p (x, y) of features and labels with …

We propose a novel approach to video anomaly detection: we treat feature vectors extracted
from videos as realizations of a random variable with a fixed distribution and model this …

Despite the recent advances in out-of-distribution (OOD) detection, anomaly detection, and
uncertainty estimation tasks, there do not exist a task-agnostic and post-hoc approach. To …

Deep learning models perform unreliably when the data come from a distribution different
from the training one. In critical applications such as medical imaging, out-of-distribution …

Detecting out-of-distribution (OOD) samples is a problem of practical importance for a
reliable use of deep neural networks (DNNs) in production settings. The corollary to this …