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 …

With a long history of traditional Graph Anomaly Detection (GAD) algorithms and recently
popular Graph Neural Networks (GNNs), it is still not clear (1) how they perform under a …

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 anomaly detection methods learn representations that separate between normal and
anomalous images. Although self-supervised representation learning is commonly used …

We introduce Neural Contextual Anomaly Detection (NCAD), a framework for anomaly
detection on time series that scales seamlessly from the unsupervised to supervised setting …

The classifiers that return compact acceptance regions are crucial for the success in
anomaly detection and open set recognition settings since we have to determine and reject …

Deep generative networks trained via maximum likelihood on a natural image dataset like
CIFAR10 often assign high likelihoods to images from datasets with different objects (eg …

Anomaly detection (AD) in images is a fundamental computer vision problem and refers to
identifying images that deviate significantly from normality. State-of-the-art AD algorithms …

Due to the intractability of characterizing everything that looks unlike the normal data,
anomaly detection (AD) is traditionally treated as an unsupervised problem utilizing only …