# Heuristic Hyperparameter Choice for Image Anomaly Detection

Source: [Heuristic Hyperparameter Choice for Image Anomaly Detection](<https://www.sota2.com/research/paper/68fa71551e209131cea5095c>)

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

Data updated: 2025-10-26T02:03:39.970Z

Authors: Zeyu Jiang, Jo\\~ao P. C. Bertoldo, Etienne Decenci\\\`ere

Published: 2023-07-20T19:20:35.000Z

## Abstract

Anomaly detection (AD) in images is a fundamental computer vision problem by deep learning neural network to identify images deviating significantly from normality. The deep features extracted from pretrained models have been proved to be essential for AD based on multivariate Gaussian distribution analysis. However, since models are usually pretrained on a large dataset for classification tasks such as ImageNet, they might produce lots of redundant features for AD, which increases computational cost and degrades the performance. We aim to do the dimension reduction of Negated Principal Component Analysis (NPCA) for these features. So we proposed some heuristic to choose hyperparameter of NPCA algorithm for getting as fewer components of features as possible while ensuring a good performance.

## 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.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2307.11197>)

[PDF](<https://arxiv.org/pdf/2307.11197.pdf>)