mlsanityData to 14 Oct 2026

DOC3-Deep One Class Classification using Contradictions

arXiv 2105.07636 ↗First posted 17 May 2021cs.LG cs.AI cs.CVcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 19 of its 63 references that are in the map. First posted on arXiv 17 May 2021.

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19 of 63 references.

References · 19

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
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7 more
  1. 2020MOCCA: Multi-Layer One-Class ClassificAtion for Anomaly Detectioncited by 31
  2. 2015Universum Prescription: Regularization using Unlabeled Datacited by 16
  3. 2019Attribute Restoration Framework for Anomaly Detectioncited by 16
  4. 2020Combining GANs and AutoEncoders for Efficient Anomaly Detectioncited by 8
  5. 2020ESAD: End-to-end Deep Semi-supervised Anomaly Detectioncited by 8
  6. 2018Active Anomaly Detection via Ensemblescited by 4
  7. 2016Universum Learning for SVM Regressioncited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 0; 2023: 1; 2024: 0; 2025: 0; 2026: 1.

4 papers.

  1. 2021Universum GANs: Improving GANs through contradictionscited by 1
  2. 2021A Survey on Proactive Customer Care: Enabling Science and Steps to Realize itcited by 0
  3. 2023Multi-Beholder: Biomarker Prediction for Low-Grade Glioma with Multiple Instance Learning and One-Class Classificationcited by 0
  4. 2026FARE: Forensic Acceptance Region Estimation for Catching Bait-and-Switch Image Generatorscited by 0