Heuristic Hyperparameter Choice for Image Anomaly Detection
Zeyu Jiang João P. C. Bertoldo Etienne Decencière
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.