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

Many deep (semi-) supervised neural network-based methods have been proposed for
anomaly detection, tackling the issue of limited labeled data. They have shown good …

Anomaly detection in videos refers to identifying events that rarely or shouldn't happen in a
certain context. Among all existing methods, the idea of reconstruction or future frame …

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 …

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

Support Vector Machines (SVMs) have been one of the most successful machine learning
techniques for the past decade. For anomaly detection, also a semi-supervised variant, the …

Anomaly detection, which aims to identify observations that deviate from a nominal sample,
is a challenging task for high‐dimensional data. Traditional distance‐based anomaly …

Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …

Unsupervised anomaly detection aims to build models to effectively detect unseen
anomalies by only training on the normal data. Although previous reconstruction-based …

Unsupervised anomaly detection on multi-or high-dimensional data is of great importance in
both fundamental machine learning research and industrial applications, for which density …