… Early anomaly detection is valuable, yet it can be difficult to execute reliably in practice. …
that anomaly detectors be fully automated. In this paper we propose a novel anomaly detection …

This paper proposes a novel method to detect anomalies in large datasets under a fully
unsupervised setting. The key idea behind our algorithm is to learn the representation …

… Here, we perform unsupervised learning to identify anomalies in imaging data as candidates
for markers. We propose AnoGAN, a deep convolutional generative adversarial network to …

… the anomaly detection approach over the misuse detection technique in detecting unknown
… clustering algorithms when applied to anomaly detection. Five different clustering algorithms…

… Here, we propose a fast anomaly detection technique trained on large-scale imaging data
… We perform unsupervised learning on these data to train a generative model that captures a …

… Considering the generality of misuse detection-based … in unsupervised anomaly detection
techniques. In previous research [20], we have proposed an unsupervised anomaly detection …

… We apply the proposed MemAE on various public anomaly detection datasets from
different applications. Extensive experiments prove the excellent generalization and high …

… [5] is an unsupervised network anomaly detector developed in our laboratory which
demonstrated good detection performance. It relies on a clustering algorithm to identify anomalies. A …

… of recently proposed unsupervised algorithms for visual industrial anomaly detection covering
five … Unsupervised anomaly detection algorithm is still under continuous research and …

… can be used directly for anomaly detection, with performance … optimal transport distances
for anomaly detection, we find that … These challenges with unsupervised anomaly detection …