  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 Rademacher Complexity (ERC) of DOC3 against its
traditional inductive learning counterpart. Our empirical results demonstrate
the efficacy of DOC3 compared to popular baseline algorithms on several
real-life data sets.
