Anomaly detection (AD) plays a crucial role in various domains, including cybersecurity,
finance, and healthcare, by identifying patterns or events that deviate from normal behavior …

Over the past few years, anomaly detection, a subfield of machine learning that is mainly
concerned with the detection of rare events, witnessed an immense improvement following …

Anomaly detection in videos is a significant yet challenging problem. Previous approaches
based on deep neural networks employ either reconstruction-based or prediction-based …

Modern network intrusion detection systems always utilize deep learning to improve their
intelligence and feature learning abilities. To overcome the difficulties of accessing a large …

Unsupervised anomaly detection (UAD) is to detect anomalies through learning the
distribution of normal data without labels and therefore has a wide application in medical …

Adaptive gradient methods, eg, ADAM, have achieved tremendous success in data-driven
machine learning, especially deep learning. Employing adaptive learning rates according to …

3D anomaly detection and localization is of great significance for industrial inspection. Prior
3D anomaly detection and localization methods focus on the setting that the testing data …

In this work, the concept of test-time learning is presented, wherein Machine-Learning (ML)
models are constructed by involving unlabeled test samples. Based on this concept, we …