TL;DR
D is proposed, a dual-encoder framework that separates reconstruction-oriented representation learning from one-class boundary modeling by imposing the SVDD constraint in a dedicated boundary space rather than directly on the reconstruction representation, which reduces the gradient conflict and scale imbalance between the two objectives.
Reconstruction-based detectors for multivariate time series anomaly detection typically learn a single entangled representation of normal behavior and flag anomalies based on reconstruction error. However, real-world anomalies are not only isolated spikes in a single channel but also disruptions in the relationships be...
Transformer with Variational AutoEncoder is presented, a framework that combines a dual-embedding representation pipeline, a Transformer encoder for long-range dependency modeling, and VAE-based latent regularization of encoded features to provide an effective and robust solution for long-term time-series anomaly detec...
Automated anomaly detection is increasingly important in radio spectrum monitoring, where large volumes of continuously generated measurements must be analyzed while anomalous events remain relatively rare. This study investigates reconstruction-based anomaly detection for real-world very-high-frequency (VHF) Band II r...
Anomaly detection becomes considerably harder when labels are scarce and the data are described by several heterogeneous views. A handful of labelled samples rarely delineates the normal region well, and the unlabelled pool is itself often contaminated by anomalies, so treating unlabelled data as normal and feeding the...
To incorporate the predictive prior into the reconstruction-based AE, a Bayesian fusion approach is proposed and this approach can enhance the modeling capability of the model for normal data and mitigate the over-generalization problem of AE.
Anomaly detection in high-dimensional temporal data remains a fundamental challenge in Internet of Things (IoT) security and low-altitude surveillance systems. While deep autoencoders excel at extracting compact feature representations, they lack explicit temporal modeling capabilities. Existing approaches typically re...
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