Self-Supervised Losses for One-Class Textual Anomaly Detection

Kimberly T. Mai, Toby Davies, Lewis D. Griffin

Current deep learning methods for anomaly detection in text rely on supervisory signals in inliers that may be unobtainable or bespoke architectures that are difficult to tune. We…