Gaussian Image Anomaly Detection with Greedy Eigencomponent Selection. (arXiv:2308.04944v1 [cs.CV])

Anomaly detection (AD) in images, identifying significant deviations from
normality, is a critical issue in computer vision. This paper introduces a
novel approach to dimensionality reduction for AD using pre-trained
convolutional neural network (CNN) that incorporate EfficientNet models. We
investigate the importance of component selection and propose two types of tree
search approaches, both employing a greedy strategy, for optimal eigencomponent
selection. Our study conducts three main experiments to evaluate the
effectiveness of our approach. The first experiment explores the …

anomaly anomaly detection arxiv cnn computer computer vision convolutional neural network detection dimensionality image images importance issue network neural network normality novel paper types vision