Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

Abstract ASHRAE Global Thermal Comfort Database has been extensively used for
analyzing specific thermal comfort parameters or models, evaluating subjective metrics, and …

This paper offers a comprehensive review of one-class classification (OCC), examining the
technologies and methodologies employed in its implementation. It delves into various …

Anomaly detection is of considerable importance in areas ranging from industrial production
over financial transaction to medical diagnosis. Due to the extreme imbalance of anomaly …

One-class classification aims to learn one-class models from only in-class training samples.
Because of lacking out-of-class samples during training, most conventional deep learning …

Automated tumor detection in Digital Breast Tomosynthesis (DBT) is a difficult task due to
natural tumor rarity, breast tissue variability, and high resolution. Given the scarcity of …

Monitoring systems produce and transmit large amounts of data. For an efficient
transmission, data is often compressed and autoencoders are a widely adopted neural …

Tailing is defined as an event where a suspicious person follows someone closely. We
define the problem of tailing detection from videos as an anomaly detection problem, where …

This paper aims to provide a deep neural network (DNN) considering the statistical
properties of data for robust oneclass classification. To achieve that, we take advantage of …

In this paper, we tackle automatic anomaly detection in multi-illumination and multi-focus
display images. The minute defects on the display surface are hard to spot out in RGB …