Recently, anomaly detection and localization in multimedia data have received
significant attention among the machine learning community. In real-world
applications such as medical diagnosis and industrial defect detection,
anomalies only present in a fraction of the images. To extend the
reconstruction-based anomaly detection architecture to the localized anomalies,
we propose a self-supervised learning approach through random masking and then
restoring, named Self-Supervised Masking (SSM) for unsupervised anomaly
detection and localization. SSM not only enhances the training of the
inpainting network but also leads to great improvement in the efficiency of
mask prediction at inference. Through random masking, each image is augmented
into a diverse set of training triplets, thus enabling the autoencoder to learn
to reconstruct with masks of various sizes and shapes during training. To
improve the efficiency and effectiveness of anomaly detection and localization
at inference, we propose a novel progressive mask refinement approach that
progressively uncovers the normal regions and finally locates the anomalous
regions. The proposed SSM method outperforms several state-of-the-arts for both
anomaly detection and anomaly localization, achieving 98.3% AUC on Retinal-OCT
and 93.9% AUC on MVTec AD, respectively.

这篇论文提出了一种名为 “自监督遮盖” 的无监督异常检测和定位方法，通过随机遮盖和恢复来扩展基于重建的异常检测架构，并引入了渐进式遮盖细化方法来进一步提高检测和定位效率和效果。