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.

翻译：图像异常检测（AD）是计算机视觉中的一个基本问题，它通过深度学习神经网络识别显著偏离正常分布的图像。基于多元高斯分布分析的预训练模型深层特征已被证明对异常检测至关重要。然而，由于这些模型通常在ImageNet等大规模数据集上针对分类任务进行预训练，它们可能为异常检测产生大量冗余特征，这既增加了计算成本又降低了性能。本文旨在对这些特征进行负主成分分析（NPCA）的降维处理。为此，我们提出了一些启发式方法，用于选择NPCA算法的超参数，从而在保证良好性能的前提下，尽可能减少特征分量数量。