# DOC3-Deep One Class Classification using Contradictions

arXiv: https://arxiv.org/abs/2105.07636 · HTML: https://mlsanity.com/paper/2105.07636/

## 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.

## Builds on (most cited first, 19 of 19)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Deep Anomaly Detection with Outlier Exposure](https://mlsanity.com/paper/1812.04606/) (2018)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Norm-Based Capacity Control in Neural Networks](https://mlsanity.com/paper/1503.00036/) (2015)
- [Deep Semi-Supervised Anomaly Detection](https://mlsanity.com/paper/1906.02694/) (2019)
- [Classification-Based Anomaly Detection for General Data](https://mlsanity.com/paper/2005.02359/) (2020)
- [Efficient GAN-Based Anomaly Detection](https://mlsanity.com/paper/1802.06222/) (2018)
- [DROCC: Deep Robust One-Class Classification](https://mlsanity.com/paper/2002.12718/) (2020)
- [AdaNet: Adaptive Structural Learning of Artificial Neural Networks](https://mlsanity.com/paper/1607.01097/) (2016)
- [MOCCA: Multi-Layer One-Class ClassificAtion for Anomaly Detection](https://mlsanity.com/paper/2012.12111/) (2020)
- [Universum Prescription: Regularization using Unlabeled Data](https://mlsanity.com/paper/1511.03719/) (2015)
- [Attribute Restoration Framework for Anomaly Detection](https://mlsanity.com/paper/1911.10676/) (2019)
- [Combining GANs and AutoEncoders for Efficient Anomaly Detection](https://mlsanity.com/paper/2011.08102/) (2020)
- [ESAD: End-to-end Deep Semi-supervised Anomaly Detection](https://mlsanity.com/paper/2012.04905/) (2020)
- [Active Anomaly Detection via Ensembles](https://mlsanity.com/paper/1809.06477/) (2018)
- [Universum Learning for SVM Regression](https://mlsanity.com/paper/1605.08497/) (2016)

## Built on by (most cited first, 4 of 4)

- [Universum GANs: Improving GANs through contradictions](https://mlsanity.com/paper/2106.09946/) (2021)
- [A Survey on Proactive Customer Care: Enabling Science and Steps to Realize it](https://mlsanity.com/paper/2110.05015/) (2021)
- [Multi-Beholder: Biomarker Prediction for Low-Grade Glioma with Multiple Instance Learning and One-Class Classification](https://mlsanity.com/paper/2310.07464/) (2023)
- [FARE: Forensic Acceptance Region Estimation for Catching Bait-and-Switch Image Generators](https://mlsanity.com/paper/2609.30982/) (2026)

## 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.