# Detection of out-of-distribution samples using binary neuron activation patterns

arXiv: https://arxiv.org/abs/2212.14268 · HTML: https://mlsanity.com/paper/2212.14268/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 55 references that are in the map.
- First posted on arXiv 29 Dec 2022.

## Builds on (most cited first, 30 of 31)

- [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)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift](https://mlsanity.com/paper/1906.02530/) (2019)
- [Deep Anomaly Detection with Outlier Exposure](https://mlsanity.com/paper/1812.04606/) (2018)
- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015)
- [ReAct: Out-of-distribution Detection With Rectified Activations](https://mlsanity.com/paper/2111.12797/) (2021)
- [PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications](https://mlsanity.com/paper/1701.05517/) (2017)
- [On the Importance of Gradients for Detecting Distributional Shifts in the Wild](https://mlsanity.com/paper/2110.00218/) (2021)
- [Extremely Simple Activation Shaping for Out-of-Distribution Detection](https://mlsanity.com/paper/2209.09858/) (2022)
- [PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation](https://mlsanity.com/paper/2010.05903/) (2020)
- [Deep ReLU Networks Have Surprisingly Few Activation Patterns](https://mlsanity.com/paper/1906.00904/) (2019)
- [Mean-Shifted Contrastive Loss for Anomaly Detection](https://mlsanity.com/paper/2106.03844/) (2021)
- [Sampling-free Epistemic Uncertainty Estimation Using Approximated Variance Propagation](https://mlsanity.com/paper/1908.00598/) (2019)
- [Outside the Box: Abstraction-Based Monitoring of Neural Networks](https://mlsanity.com/paper/1911.09032/) (2019)
- [Runtime Monitoring Neuron Activation Patterns](https://mlsanity.com/paper/1809.06573/) (2018)
- [Activation Analysis of a Byte-Based Deep Neural Network for Malware Classification](https://mlsanity.com/paper/1903.04717/) (2019)
- [Stationary Activations for Uncertainty Calibration in Deep Learning](https://mlsanity.com/paper/2010.09494/) (2020)
- [Prior Activation Distribution (PAD): A Versatile Representation to Utilize DNN Hidden Units](https://mlsanity.com/paper/1907.02711/) (2019)

## Built on by (most cited first, 15 of 15)

- [Gradient-Regularized Out-of-Distribution Detection](https://mlsanity.com/paper/2404.12368/) (2024)
- [Uncertainty-Guided Appearance-Motion Association Network for Out-of-Distribution Action Detection](https://mlsanity.com/paper/2409.09953/) (2024)
- [Distribution Shifts at Scale: Out-of-distribution Detection in Earth Observation](https://mlsanity.com/paper/2412.13394/) (2024)
- [AdaSCALE: Adaptive Scaling for OOD Detection](https://mlsanity.com/paper/2503.08023/) (2025)
- [Out-of-Distribution Detection using Neural Activation Prior](https://mlsanity.com/paper/2402.18162/) (2024)
- [Monitizer: Automating Design and Evaluation of Neural Network Monitors](https://mlsanity.com/paper/2405.10350/) (2024)
- [Runtime Monitoring DNN-Based Perception](https://mlsanity.com/paper/2310.03999/) (2023)
- [Improving Out-of-Distribution Detection with Disentangled Foreground and Background Features](https://mlsanity.com/paper/2303.08727/) (2023)
- [Detecting Out-of-distribution Objects Using Neuron Activation Patterns](https://mlsanity.com/paper/2307.16433/) (2023)
- [Distilling the Unknown to Unveil Certainty](https://mlsanity.com/paper/2311.07975/) (2023)
- [On the Detection of Anomalous or Out-Of-Distribution Data in Vision Models Using Statistical Techniques](https://mlsanity.com/paper/2403.15497/) (2024)
- [Resilience and Security of Deep Neural Networks Against Intentional and Unintentional Perturbations: Survey and Research Challenges](https://mlsanity.com/paper/2408.00193/) (2024)
- [LoRA-BAM: Input Filtering for Fine-tuned LLMs via Boxed Abstraction Monitors over LoRA Layers](https://mlsanity.com/paper/2506.00998/) (2025)
- [A New Framework to Analyse the Distributional Robustness of Deep Neural Networks](https://mlsanity.com/paper/2605.21313/) (2026)
- [Unified Multi-Layer Subspace Modeling for Cross-Domain OOD Detection](https://mlsanity.com/paper/2609.06785/) (2026)

## Abstract (arXiv)

Deep neural networks (DNN) have outstanding performance in various applications. Despite numerous efforts of the research community, out-of-distribution (OOD) samples remain a significant limitation of DNN classifiers. The ability to identify previously unseen inputs as novel is crucial in safety-critical applications such as self-driving cars, unmanned aerial vehicles, and robots. Existing approaches to detect OOD samples treat a DNN as a black box and evaluate the confidence score of the output predictions. Unfortunately, this method frequently fails, because DNNs are not trained to reduce their confidence for OOD inputs. In this work, we introduce a novel method for OOD detection. Our method is motivated by theoretical analysis of neuron activation patterns (NAP) in ReLU-based architectures. The proposed method does not introduce a high computational overhead due to the binary representation of the activation patterns extracted from convolutional layers. The extensive empirical evaluation proves its high performance on various DNN architectures and seven image datasets.