We consider the problem of anomaly detection in images, and present a new detection
technique. Given a sample of images, all known to belong to a``normal''class (eg, dogs), we …

Generative adversarial networks (GANs) are able to model the complex highdimensional
distributions of real-world data, which suggests they could be effective for anomaly …

Novelty detection is commonly referred as the discrimination of observations that do not
conform to a learned model of regularity. Despite its importance in different application …

Anomaly detection is a significant and hence well-studied problem. However, developing
effective anomaly detection methods for complex and high-dimensional data remains a …

Open-set classification is a problem of handling'unknown'classes that are not contained in
the training dataset, whereas traditional classifiers assume that only known classes appear …

In recent years, proposed studies on time-series anomaly detection (TAD) report high F1
scores on benchmark TAD datasets, giving the impression of clear improvements in TAD …

Out-of-distribution detection is crucial to the safe deployment of machine learning systems.
Currently, unsupervised out-of-distribution detection is dominated by generative-based …

Video anomaly detection aims to detect abnormal segments in a video sequence, which is a
key problem in video surveillance. Based on deep prediction methods, we propose a …

With the recent advances in deep neural networks, anomaly detection in multimedia has
received much attention in the computer vision community. While reconstruction-based …

Abstract Video Anomaly Detection (VAD) is an important topic in computer vision. Motivated
by the recent advances in self-supervised learning, this paper addresses VAD by solving an …