mlsanityData to 7 Oct 2026

Rule-based Out-Of-Distribution Detection

arXiv 2303.01860 ↗First posted 3 Mar 2023cs.AIcited by 3

Not on the map. Why?

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 43 references that are in the map. First posted on arXiv 3 Mar 2023.

Builds on

11 of 43 references.

References · 11

  1. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  2. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,472
  3. 2020Energy-based Out-of-distribution Detectioncited by 1,296
  4. 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 596
  5. 2021SSD: A Unified Framework for Self-Supervised Outlier Detectioncited by 264
  6. 2021An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Drivingcited by 33
  7. 2022Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantitiescited by 23
  8. 2019Detecting Out-of-Distribution Inputs in Deep Neural Networks Using an Early-Layer Outputcited by 20
  9. 2021Prediction error quantification through probabilistic scaling -- EXTENDED VERSIONcited by 4
  10. 2021Class-wise Thresholding for Robust Out-of-Distribution Detectioncited by 3
  11. 2021Out-of-Distribution Detection Using Outlier Detection Methodscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024Adaptive Hierarchical Graph Cut for Multi-granularity Out-of-distribution Detectioncited by 8
  2. 2026FedLAS: Feature-Modulated Bidirectional Label Smoothing for Neural Network Calibrationcited by 1
  3. 2025Hierarchical Adaptive networks with Task vectors for Test-Time Adaptationcited by 0