  Currently, deep learning-based visual inspection has been highly successful
with the help of supervised learning methods. However, in real industrial
scenarios, the scarcity of defect samples, the cost of annotation, and the lack
of a priori knowledge of defects may render supervised-based methods
ineffective. In recent years, unsupervised anomaly localization algorithms have
become more widely used in industrial inspection tasks. This paper aims to help
researchers in this field by comprehensively surveying recent achievements in
unsupervised anomaly localization in industrial images using deep learning. The
survey reviews more than 120 significant publications covering different
aspects of anomaly localization, mainly covering various concepts, challenges,
taxonomies, benchmark datasets, and quantitative performance comparisons of the
methods reviewed. In reviewing the achievements to date, this paper provides
detailed predictions and analysis of several future research directions. This
review provides detailed technical information for researchers interested in
industrial anomaly localization and who wish to apply it to the localization of
anomalies in other fields.
