The recent rapid development of deep learning has laid a milestone in industrial image
anomaly detection (IAD). In this paper, we provide a comprehensive review of deep learning …

Given a long list of anomaly detection algorithms developed in the last few decades, how do
they perform with regard to (i) varying levels of supervision,(ii) different types of anomalies …

Although mainstream unsupervised anomaly detection (AD) algorithms perform well in
academic datasets, their performance is limited in practical application due to the ideal …

Due to the unsupervised nature of anomaly detection, the key to fueling deep models is
finding supervisory signals. Different from current reconstruction-guided generative models …

Time series anomaly detection is instrumental in maintaining system availability in various
domains. Current work in this research line mainly focuses on learning data normality …

Selecting informative data points for expert feedback can significantly improve the
performance of anomaly detection (AD) in various contexts, such as medical diagnostics or …

Anomaly detection (AD), aiming to find samples that deviate from the training distribution, is
essential in safety-critical applications. Though recent self-supervised learning based …

Anomaly detection (AD) plays a crucial role in many safety-critical application domains. The
challenge of adapting an anomaly detector to drift in the normal data distribution, especially …

Anomaly detection aims at identifying deviant samples from the normal data distribution.
Contrastive learning has provided a successful way to sample representation that enables …

While existing reconstruction-based multivariate time series (MTS) anomaly detection
methods demonstrate advanced performance on many challenging real-world datasets, they …