In this paper, we consider the problem of anomaly detection. Previous studies mostly deal
with this task in either supervised or unsupervised manner according to whether label …

This article provides a thorough meta-analysis of the anomaly detection problem. To
accomplish this we first identify approaches to benchmarking anomaly detection algorithms …

Similarity representation plays a central role in increasingly popular anomaly detection
techniques, which have been successfully applied in various realistic scenes. Until now …

Anomaly detection in videos refers to identifying events that rarely or shouldn't happen in a
certain context. Among all existing methods, the idea of reconstruction or future frame …

We consider the problem of anomaly detection with a small set of partially labeled anomaly
examples and a large-scale unlabeled dataset. This is a common scenario in many …

Anomaly detection is an important problem that has been researched within diverse
research areas and application domains. Many anomaly detection techniques have been …

Anomaly detection presents a unique challenge in machine learning, due to the scarcity of
labeled anomaly data. Recent work attempts to mitigate such problems by augmenting …

We address a critical yet largely unsolved anomaly detection problem, in which we aim to
learn detection models from a small set of partially labeled anomalies and a large-scale …

Anomaly detection, which aims to discover anomalous events, defined as having a low
likelihood of occurrence, from surveillance videos, has attracted increasing interest and is …

3. Conclusions In this Special Issue titled “Unsupervised Anomaly Detection” of Applied
Sciences, a total of 12 papers (11 research articles and one review paper) are published …