DOC3-Deep One Class Classification using Contradictions

This paper introduces the notion of learning from contradictions (a.k.a Universum learning) for deep one class classification problems. We formalize this notion for the widely adopted one class large-margin loss, and propose the Deep One Class Classification using Contradictions (DOC3) algorithm. We show that learning from contradictions incurs lower generalization error by comparing the Empirical Radamacher Complexity (ERC) of DOC3 against its traditional inductive learning counterpart. Our empirical results demonstrate the efficacy of DOC3 algorithm achieving > 30% for CIFAR-10 and >50% for MV-Tec AD data sets in test AUCs compared to its inductive learning counterpart and in many cases improving the state-of-the-art in anomaly detection.