As the size and complexity of high-performance computing (HPC) systems keep growing,
scientists' ability to trust the data produced is paramount due to potential data corruption for …

Anomaly detection has a wide range of applications in security area such as network
monitoring and smart city/campus construction. It has become an active research issue of …

Anomaly detection is one of the fundamental problems within diverse research areas and
application domains. In comparison with most sparse representation based anomaly …

Anomaly detection methods exploiting autoencoders (AE) have shown good performances.
Unfortunately, deep non-linear architectures are able to perform high dimensionality …

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

Density estimation is a widely used method to perform unsupervised anomaly detection. By
learning the density function, data points with relatively low densities are classified as …

In this paper, the recently published benchmark of Goldstein and Uchida [3] for
unsupervised anomaly detection is extended with three anomaly detection techniques …

This work presents a novel method called dense projection for unsupervised anomaly
detection (DPAD). The main idea is maximizing the local density of (normal) training data …

This paper explores semi-supervised anomaly detection, a more practical setting for
anomaly detection where a small additional set of labeled samples are provided. We …

Existing semi-supervised anomaly detection methods usually use a large amount of labeled
normal data for training, which have the problem of high labeling costs. Only a few semi …