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 …

Video anomaly detection is well investigated in weakly supervised and one-class
classification (OCC) settings. However, unsupervised video anomaly detection is quite …

In recent years, generative machine learning approaches have attracted significant attention
as an enabling approach for designing novel molecular materials with minimal design bias …

Image anomaly detection aims to detect out-of-distribution instances. Most existing methods
treat anomaly detection as an unsupervised task because anomalous training data and …

Under an open dynamic environment, a challenging task in object detection is to determine
whether samples belong to a known class. Novelty detection can be exploited to identify …

Abstract Video Anomaly Detection (VAD) is an open-set recognition task which is usually
formulated as a one-class classification (OCC) problem where training data is comprised of …

Novelty detection is usually defined as the identification of new or abnormal objects
(outliers) from the normal ones (inliers), which has wide potential applications in the real …

Due to the rare occurrence of anomalous events, a typical approach to anomaly detection is
to train an autoencoder (AE) with normal data only so that it learns the patterns or …

Current approaches to novelty or anomaly detection are based on deep neural networks.
Despite their effectiveness, neural networks are also vulnerable to imperceptible …

Under the X-ray scanning, mobile phone explosive modified at the battery is stealthy, which
increases the difficulty of security inspections to detect prohibited phones. This critical issue …