Detecting anomalies in images and video is an essential task for multiple real-world problems, including industrial inspection, computer-assisted diagnosis, and environmental monitoring. Anomaly detection is typically formulated as a one-class classification problem, where the training data consists solely of nominal values, leaving methods built on this assumption susceptible to training label noise. We present a dataset folding method that transforms an arbitrary one-class classifier-based anomaly detector into a fully unsupervised method. This is achieved by making a set of key weak assumptions: that anomalies are uncommon in the training dataset and generally heterogeneous. These assumptions enable us to utilize multiple independently trained instances of a one-class classifier to filter the training dataset for anomalies. This transformation requires no modifications to the underlying anomaly detector; the only changes are algorithmically selected data subsets used for training. We demonstrate that our method can transform a wide variety of one-class classifier anomaly detectors for both images and videos into unsupervised ones. Our method creates the first unsupervised logical anomaly detectors by transforming existing methods. We also demonstrate that our method achieves state-of-the-art performance for unsupervised anomaly detection on the MVTec AD, ViSA, and MVTec Loco AD datasets. As improvements to one-class classifiers are made, our method directly transfers those improvements to the unsupervised domain, linking the domains.

翻译：图像与视频异常检测是工业检测、计算机辅助诊断和环境监测等多个现实应用中的关键任务。异常检测通常被构建为单分类问题，其训练数据仅包含正常样本，导致基于此假设的方法易受训练标签噪声的影响。本文提出一种数据集折叠方法，可将任意基于单分类器的异常检测器转换为完全无监督的方法。该方法基于一组关键弱假设：训练数据集中异常样本较为罕见且通常具有异质性。这些假设使我们能够利用多个独立训练的单分类器实例来过滤训练数据集中的异常样本。该转换无需修改底层异常检测器，仅需算法选择用于训练的数据子集。我们证明，该方法可将多种适用于图像和视频的单分类器异常检测器转换为无监督版本，并通过转换现有方法首次实现了无监督逻辑异常检测器。在MVTec AD、ViSA和MVTec Loco AD数据集上的实验表明，我们的方法在无监督异常检测中达到了最先进的性能。随着单分类器的改进，本方法可直接将这些改进迁移至无监督领域，从而建立两个领域间的关联。