Adversarial Training

Deep Anomaly Detection with Self-Supervised Learning and Adversarial Training

This paper combines self-supervised learning and adversarial training to improve deep anomaly detection, leveraging unlabeled normal data to learn representations that are robust to perturbations and more sensitive to out-of-distribution inputs. It matters because labeled anomaly data is rare in practice, making self-supervised approaches essential for real-world deployment.