# Heuristic Hyperparameter Choice for Image Anomaly Detection

arXiv: https://arxiv.org/abs/2307.11197 · HTML: https://mlsanity.com/paper/2307.11197/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 7 references that are in the map.
- First posted on arXiv 20 Jul 2023.

## Builds on (most cited first, 4 of 4)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection](https://mlsanity.com/paper/2108.07610/) (2021)
- [Selecting the number of principal components: estimation of the true rank of a noisy matrix](https://mlsanity.com/paper/1410.8260/) (2014)
- [Discovering State Variables Hidden in Experimental Data](https://mlsanity.com/paper/2112.10755/) (2021)

## Built on by (most cited first, 1 of 1)

- [Gaussian Image Anomaly Detection with Greedy Eigencomponent Selection](https://mlsanity.com/paper/2308.04944/) (2023)

## Abstract (arXiv)

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.