One of the opening steps towards obtaining a reasoned analysis is the detection of outlaying
observations. Even if outliers are often considered as a miscalculation or noise, they may …

Outlier detection is a statistical procedure that aims to find suspicious events or items that
are different from the normal form of a dataset. It has drawn considerable interest in the field …

Nearest neighbor (kNN) methods utilizing deep pre-trained features exhibit very strong
anomaly detection performance when applied to entire images. A limitation of kNN methods …

Anomaly detection methods require high-quality features. In recent years, the anomaly
detection community has attempted to obtain better features using advances in deep self …

Detecting outliers is a significant problem that has been studied in various research and
application areas. Researchers continue to design robust schemes to provide solutions to …

Nowadays, multi-sensor technologies are applied in many fields, eg, Health Care (HC),
Human Activity Recognition (HAR), and Industrial Control System (ICS). These sensors can …

PyOD is an open-source Python toolbox for performing scalable outlier detection on
multivariate data. Uniquely, it provides access to a wide range of outlier detection …

We present a two-stage framework for deep one-class classification. We first learn self-
supervised representations from one-class data, and then build one-class classifiers on …

The so-called “smartization” of manufacturing industries has been conceived as the fourth
industrial revolution or Industry 4.0, a paradigm shift propelled by the upsurge and …

Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …