Deep Generative Model using Unregularized Score for Anomaly Detection with Heterogeneous Complexity

Abstract--Accurate and automated detection of anomalous samples in a natural image dataset can be accomplished with a probabilistic model for end-to-end modeling of images. Such images have heterogeneous complexity, however, and a probabilistic model overlooks simply shaped objects with small anomalies. This is because the probabilistic model assigns undesirably lower likelihoods to complexly shaped objects that are nevertheless consistent with set standards. To overcome this difficulty, we propose an unregularized score for deep generative models (DGMs), which are generative models leveraging deep neural networks. We found that the regularization terms of the DGMs considerably influence the anomaly score depending on the complexity of the samples. By removing these terms, we obtain an unregularized score, which we evaluated on a toy dataset and real-world manufacturing datasets. Empirical results demonstrate that the unregularized score is robust to the inherent complexity of samples and can be used to better detect anomalies. Image-based anomaly detection has recently attracted considerable attention in the field of machine learning. This technique can be used to detect pedestrians behaving abnormally from surveillance video in order to prevent accidents [1], [2], or to detect lesions in medical images to provide early diagnosis [3]. In manufacturing plants, moreover, image-based anomaly detection can reject products not coincident with set standards.