DOC3-Deep One Class Classification using Contradictions
Machine Learning
2022-05-24 v2 Artificial Intelligence
Computer Vision and Pattern Recognition
Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.2105.07636,
title = {DOC3-Deep One Class Classification using Contradictions},
author = {Sauptik Dhar and Bernardo Gonzalez Torres},
journal= {arXiv preprint arXiv:2105.07636},
year = {2022}
}
Comments
Deep Learning, Anomaly Detection, Visual Inspection, Learning from Contradictions, Disjoint Auxiliary, Outlier Exposure, MVTec-AD