When Rule Violations Are Rare: Chimera Training for Logical Anomaly Detection

This paper proposes a method for anomaly detection called Chimera Training, focusing on violations of semantic constraints given as logical rules over learned visual concepts. It employs a neural rule evaluator that compiles constraints into directed acyclic graphs, learning logical operators to calculate rule-satisfaction probabilities, even with scarce training data for actual violations.