# Rule-based Out-Of-Distribution Detection

arXiv: https://arxiv.org/abs/2303.01860 · HTML: https://mlsanity.com/paper/2303.01860/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 3 Mar 2023.

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

- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [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)
- [Out-of-Distribution Detection with Deep Nearest Neighbors](https://mlsanity.com/paper/2204.06507/) (2022)
- [SSD: A Unified Framework for Self-Supervised Outlier Detection](https://mlsanity.com/paper/2103.12051/) (2021)
- [An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving](https://mlsanity.com/paper/2103.03678/) (2021)
- [Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities](https://mlsanity.com/paper/2206.09880/) (2022)
- [Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Output](https://mlsanity.com/paper/1910.10307/) (2019)
- [Prediction error quantification through probabilistic scaling -- EXTENDED VERSION](https://mlsanity.com/paper/2105.14187/) (2021)
- [Class-wise Thresholding for Robust Out-of-Distribution Detection](https://mlsanity.com/paper/2110.15292/) (2021)
- [Out-of-Distribution Detection Using Outlier Detection Methods](https://mlsanity.com/paper/2108.08218/) (2021)

## Built on by (most cited first, 3 of 3)

- [Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detection](https://mlsanity.com/paper/2412.15668/) (2024)
- [FedLAS: Feature-Modulated Bidirectional Label Smoothing for Neural Network Calibration](https://mlsanity.com/paper/2606.28654/) (2026)
- [Hierarchical Adaptive networks with Task vectors for Test-Time Adaptation](https://mlsanity.com/paper/2508.09223/) (2025)

## Abstract (arXiv)

Out-of-distribution detection is one of the most critical issue in the deployment of machine learning. The data analyst must assure that data in operation should be compliant with the training phase as well as understand if the environment has changed in a way that autonomous decisions would not be safe anymore. The method of the paper is based on eXplainable Artificial Intelligence (XAI); it takes into account different metrics to identify any resemblance between in-distribution and out of, as seen by the XAI model. The approach is non-parametric and distributional assumption free. The validation over complex scenarios (predictive maintenance, vehicle platooning, covert channels in cybersecurity) corroborates both precision in detection and evaluation of training-operation conditions proximity. Results are available via open source and open data at the following link: https://github.com/giacomo97cnr/Rule-based-ODD.