# O2D2: Out-Of-Distribution Detector to Capture Undecidable Trials in Authorship Verification

arXiv: https://arxiv.org/abs/2106.15825 · HTML: https://mlsanity.com/paper/2106.15825/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 30 Jun 2021.

## Builds on (most cited first, 11 of 11)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Regularizing Neural Networks by Penalizing Confident Output Distributions](https://mlsanity.com/paper/1701.06548/) (2017)
- [Explainable Authorship Verification in Social Media via Attention-based Similarity Learning](https://mlsanity.com/paper/1910.08144/) (2019)
- [The Importance of Suppressing Domain Style in Authorship Analysis](https://mlsanity.com/paper/2005.14714/) (2020)
- [Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix](https://mlsanity.com/paper/1705.03995/) (2017)
- [Calibrating Deep Neural Network Classifiers on Out-of-Distribution Datasets](https://mlsanity.com/paper/2006.08914/) (2020)
- [Deep Bayes Factor Scoring for Authorship Verification](https://mlsanity.com/paper/2008.10105/) (2020)
- [Self-Calibrating Neural-Probabilistic Model for Authorship Verification Under Covariate Shift](https://mlsanity.com/paper/2106.11196/) (2021)

## Built on by (most cited first, 6 of 6)

- [On the State of the Art in Authorship Attribution and Authorship Verification](https://mlsanity.com/paper/2209.06869/) (2022)
- [VeriDark: A Large-Scale Benchmark for Authorship Verification on the Dark Web](https://mlsanity.com/paper/2207.03477/) (2022)
- [Rethinking the Authorship Verification Experimental Setups](https://mlsanity.com/paper/2112.05125/) (2021)
- [Addressing Topic Leakage in Cross-Topic Evaluation for Authorship Verification](https://mlsanity.com/paper/2407.19164/) (2024)
- [Authorship identification under domain shift: a survey of stylistic measures and learned author representations](https://mlsanity.com/paper/2310.00436/) (2023)
- [Contrastive Learning for Authorship Verification](https://mlsanity.com/paper/2609.28471/) (2026)

## Abstract (arXiv)

The PAN 2021 authorship verification (AV) challenge is part of a three-year strategy, moving from a cross-topic/closed-set AV task to a cross-topic/open-set AV task over a collection of fanfiction texts. In this work, we present a novel hybrid neural-probabilistic framework that is designed to tackle the challenges of the 2021 task. Our system is based on our 2020 winning submission, with updates to significantly reduce sensitivities to topical variations and to further improve the system's calibration by means of an uncertainty-adaptation layer. Our framework additionally includes an out-of-distribution detector (O2D2) for defining non-responses. Our proposed system outperformed all other systems that participated in the PAN 2021 AV task.