Anomalous data are such data that deviate from a large number of normal data points, which
often have negative impacts on various systems. Current anomaly detection technology …

Anomaly detection is an essential task for different fields in the real world. The imbalanced
data and lack of labels make the task challenging. Deep learning models based on …

Anomaly detection is an important problem for recent advances in machine learning. To this
end, many attempts have emerged to detect unknown anomalies of the images by learning …

High-dimensional problem domains pose significant challenges for anomaly detection. The
presence of irrelevant features can conceal the presence of anomalies. This problem, known …

Abstract We propose the Autoencoding Binary Classifiers (ABC), a novel supervised
anomaly detector based on the Autoencoder (AE). There are two main approaches in …

Deep learning is increasingly used for unsupervised feature extraction and anomaly
detection in big datasets. Most deep learning based anomaly detection techniques …

Unsupervised techniques typically rely on the probability density distribution of the data to
detect anomalies, where objects with low probability density are considered to be abnormal …

Anomaly detection is a key task in Prognostics and Health Management (PHM) system.
Specially, in most practical applications, the lack of labels often exists which makes the …

With the increasing population of Industry 4.0, industrial big data (IBD) has become a hotly
discussed topic in digital and intelligent industry field. The security problem existing in the …

Recently, deep generative models have become increasingly popular in unsupervised
anomaly detection. However, deep generative models aim at recovering the data distribution …