  In this paper, we address the problem of image anomaly detection and
segmentation. Anomaly detection involves making a binary decision as to whether
an input image contains an anomaly, and anomaly segmentation aims to locate the
anomaly on the pixel level. Support vector data description (SVDD) is a
long-standing algorithm used for an anomaly detection, and we extend its deep
learning variant to the patch-based method using self-supervised learning. This
extension enables anomaly segmentation and improves detection performance. As a
result, anomaly detection and segmentation performances measured in AUROC on
MVTec AD dataset increased by 9.8% and 7.0%, respectively, compared to the
previous state-of-the-art methods. Our results indicate the efficacy of the
proposed method and its potential for industrial application. Detailed analysis
of the proposed method offers insights regarding its behavior, and the code is
available online.
