FRE: A Fast Method For Anomaly Detection And Segmentation
Ibrahima Ndiour (Intel),* Nilesh A Ahuja (Intel), Ergin U Genc (Intel), Omesh Tickoo (Intel)
The 34th British Machine Vision Conference
Abstract
This paper presents a fast and principled approach to address the challenge of visual anomaly detection and segmentation. Our method operates under the assumption of having access solely to anomaly-free training data while aiming to identify anomalies of an arbitrary nature on test data. We build upon prior research and present a generalized approach that utilizes a shallow linear autoencoder to perform out-of-distribution detection on the intermediate features generated by a pre-trained deep neural network. More specifically, we compute the feature reconstruction error (FRE) and establish it as principled measure of uncertainty. We rigorously connect our technique to the theory of linear auto-associative networks to provide a solid theoretical foundation and to offer multiple practical implementation strategies. Furthermore, extending the FRE concept to convolutional layers, we derive FRE maps that provide precise pixel-level spatial localization of the anomalies within images, effectively achieving segmentation. Extensive experimentation demonstrates that our method outperforms the current state of the art. It excels in terms of speed, robustness, and remarkable insensitivity to parameterization. We make our code available at: https://intellabs.github.io/dfmCitation
@inproceedings{Ndiour_2023_BMVC,
author = {Ibrahima Ndiour and Nilesh A Ahuja and Ergin U Genc and Omesh Tickoo},
title = {FRE: A Fast Method For Anomaly Detection And Segmentation},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year = {2023},
url = {https://bmvc2022.mpi-inf.mpg.de/BMVC2023/0614.pdf}
}
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