# Adaptive Label Smoothing for Out-of-Distribution Detection

arXiv: https://arxiv.org/abs/2410.06134 · HTML: https://mlsanity.com/paper/2410.06134/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 41 references that are in the map.
- First posted on arXiv 8 Oct 2024.

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [Predictive Uncertainty Estimation via Prior Networks](https://mlsanity.com/paper/1802.10501/) (2018)
- [ReAct: Out-of-distribution Detection With Rectified Activations](https://mlsanity.com/paper/2111.12797/) (2021)
- [ViM: Out-Of-Distribution with Virtual-logit Matching](https://mlsanity.com/paper/2203.10807/) (2022)
- [On the Importance of Gradients for Detecting Distributional Shifts in the Wild](https://mlsanity.com/paper/2110.00218/) (2021)
- [Recent Advances in Open Set Recognition: A Survey](https://mlsanity.com/paper/1811.08581/) (2018)
- [Reducing Network Agnostophobia](https://mlsanity.com/paper/1811.04110/) (2018)
- [A Comprehensive Survey of Image Augmentation Techniques for Deep Learning](https://mlsanity.com/paper/2205.01491/) (2022)
- [Delving Deep into Label Smoothing](https://mlsanity.com/paper/2011.12562/) (2020)
- [Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation](https://mlsanity.com/paper/2110.03051/) (2021)
- [Deep Transfer Learning for Multiple Class Novelty Detection](https://mlsanity.com/paper/1903.02196/) (2019)
- [Adaptive Label Smoothing](https://mlsanity.com/paper/2009.06432/) (2020)
- [Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks](https://mlsanity.com/paper/2007.03212/) (2020)
- [Paddy Doctor: A Visual Image Dataset for Automated Paddy Disease Classification and Benchmarking](https://mlsanity.com/paper/2205.11108/) (2022)
- [What are the most important statistical ideas of the past 50 years?](https://mlsanity.com/paper/2012.00174/) (2020)

## Built on by (most cited first, 2 of 2)

- [Variational Learning Induces Adaptive Label Smoothing](https://mlsanity.com/paper/2502.07273/) (2025)
- [Unknown-Traffic Detection, Calibration and Shortcut Reliance in Distilled Encrypted-Traffic Classifiers over One Year](https://mlsanity.com/paper/2609.31141/) (2026)

## Abstract (arXiv)

Out-of-distribution (OOD) detection, which aims to distinguish unknown classes from known classes, has received increasing attention recently. A main challenge within is the unavailable of samples from the unknown classes in the training process, and an effective strategy is to improve the performance for known classes. Using beneficial strategies such as data augmentation and longer training is thus a way to improve OOD detection. However, label smoothing, an effective method for classifying known classes, degrades the performance of OOD detection, and this phenomenon is under exploration. In this paper, we first analyze that the limited and predefined learning target in label smoothing results in the smaller maximal probability and logit, which further leads to worse OOD detection performance. To mitigate this issue, we then propose a novel regularization method, called adaptive label smoothing (ALS), and the core is to push the non-true classes to have same probabilities whereas the maximal probability is neither fixed nor limited. Extensive experimental results in six datasets with two backbones suggest that ALS contributes to classifying known samples and discerning unknown samples with clear margins. Our code will be available to the public.