Open access
Jul 2026
Memory-Augmented Autoencoding with Self-Supervised Learning for Unsupervised Detection of Abnormal Physiological Signals.
MAGE is a novel unsupervised anomaly detection framework that integrates multi head memory gating, self-supervised learning, and adversarial training within a unified convolutional autoencoder architecture that consistently outperforms state-of-the-art baselines in standard within-dataset evaluation settings.
Zexin Wang, Lin Shi, Shoukun Xu et al.
· IEEE journal of biomedical a... · 0 citations