# Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks

arXiv: https://arxiv.org/abs/2007.03212 · HTML: https://mlsanity.com/paper/2007.03212/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2020.

## Built on by (most cited first, 5 of 5)

- [Ranking Info Noise Contrastive Estimation: Boosting Contrastive Learning via Ranked Positives](https://mlsanity.com/paper/2201.11736/) (2022)
- [Addressing out-of-distribution label noise in webly-labelled data](https://mlsanity.com/paper/2110.13699/) (2021)
- [Adaptive Label Smoothing for Out-of-Distribution Detection](https://mlsanity.com/paper/2410.06134/) (2024)
- [Towards Robust Deep Learning using Entropic Losses](https://mlsanity.com/paper/2208.03566/) (2022)
- [Unknown-Traffic Detection, Calibration and Shortcut Reliance in Distilled Encrypted-Traffic Classifiers over One Year](https://mlsanity.com/paper/2609.31141/) (2026)

## Abstract (arXiv)

Soft labeling becomes a common output regularization for generalization and model compression of deep neural networks. However, the effect of soft labeling on out-of-distribution (OOD) detection, which is an important topic of machine learning safety, is not explored. In this study, we show that soft labeling can determine OOD detection performance. Specifically, how to regularize outputs of incorrect classes by soft labeling can deteriorate or improve OOD detection. Based on the empirical results, we postulate a future work for OOD-robust DNNs: a proper output regularization by soft labeling can construct OOD-robust DNNs without additional training of OOD samples or modifying the models, while improving classification accuracy.