Over the past decade, we have witnessed an enormous amount of research effort dedicated
to the design of efficient outlier detection techniques while taking into consideration …

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 …

Abstract Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of
machine learning systems. For instance, in autonomous driving, we would like the driving …

As spacecraft send back increasing amounts of telemetry data, improved anomaly detection
systems are needed to lessen the monitoring burden placed on operations engineers and …

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 …

Anomaly detection is the process of identifying unexpected items or events in datasets,
which differ from the norm. In contrast to standard classification tasks, anomaly detection is …

This work introduces a model that can recognize objects in images even if no training data is
available for the object class. The only necessary knowledge about unseen categories …

The monitoring of rotating machinery is an essential task in today's production processes.
Currently, several machine learning and deep learning-based modules have achieved …

Image classification systems recently made a giant leap with the advancement of deep
neural networks. However, these systems require an excessive amount of labeled data to be …

An integrated framework for density-based cluster analysis, outlier detection, and data
visualization is introduced in this article. The main module consists of an algorithm to …