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.
翻译:本文介绍了从矛盾(a.k.a.a.大学学习)中学习深层一类分类问题的概念。我们将这一概念正式确定为被广泛采纳的一类大差值损失,并提议使用自相矛盾的算法进行深层一类分类。我们表明,从矛盾中学习会产生较低的概括错误,通过将DOC3 与传统的感性学习对应方比较,将DOC3 与若干实际生活数据集的流行基线算法相比,我们的经验结果表明,DOC3 与流行的基准算法相比是有效的。