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

Source: [Detection of out-of-distribution samples using binary neuron activation patterns](<https://www.sota2.com/research/paper/68fa6ee51e209131cea42b52>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-01T16:39:35.898Z

Authors: Bartlomiej Olber, Krystian Radlak, Adam Popowicz, Michal Szczepankiewicz, Krystian Chachu{\\l}a

Published: 2022-12-29T11:42:46.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Out-of-Distribution Detection on Aggregated OOD benchmark 308 experiments (test)](<https://www.sota2.com/research/sota/out-of-distribution-detection-on-aggregated-ood-benchmark-308-experiments-test.md>): Out-of-Distribution Detection; Aggregated OOD benchmark 308 experiments (test); Latency (s): 0.008.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2212.14268>)

[PDF](<https://arxiv.org/pdf/2212.14268.pdf>)

[Implementation](<https://github.com/safednn-group/nap-ood>)