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

Anomaly detection and localization of visual data, including images and videos, are of great
significance in both machine learning academia and applied real-world scenarios. Despite …

Anomaly detection is a foundational yet difficult problem in machine learning. In this work,
we propose a new and effective framework, dubbed as SLA 2 P, for unsupervised anomaly …

The evolving unknown cyberattacks, compounded by the widespread emerging
technologies (say 5G, Internet of Things, etc.), have rapidly expanded the cyber threat …

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 …

We consider a self-supervised approach to anomaly detection in tabular data. Random
transformations are applied to the data, and then each transformation is identified based on …

The evolving unknown cyberattacks have rapidly increased the cyber threat surface.
However, since only known cyberattack samples are usually available, most existing …

Large-scale models, abundant data, and dense computation are the pivotal pillars of deep
neural networks. The present-day deep learning models have made significant strides in …

Artificial intelligence (AI) empowered by deep learning, has been profoundly transforming
the world. However, the excessive size of these models remains a central obstacle that limits …