Reconstruction-Based Anomaly Localization via Knowledge-Informed
  Self-Training
Cheng Qian Xiaoxian Lao Chunguang Li
Anomaly localization, which involves localizing anomalous regions within
images, is a significant industrial task. Reconstruction-based methods are
widely adopted for anomaly localization because of their low complexity and
high interpretability. Most existing reconstruction-based methods only use
normal samples to construct model. If anomalous samples are appropriately
utilized in the process of anomaly localization, the localization performance
can be improved. However, usually only weakly labeled anomalous samples are
available, which limits the improvement. In many cases, we can obtain some
knowledge of anomalies summarized by domain experts. Taking advantage of such
knowledge can help us better utilize the anomalous samples and thus further
improve the localization performance. In this paper, we propose a novel
reconstruction-based method named knowledge-informed self-training (KIST) which
integrates knowledge into reconstruction model through self-training.
Specifically, KIST utilizes weakly labeled anomalous samples in addition to the
normal ones and exploits knowledge to yield pixel-level pseudo-labels of the
anomalous samples. Based on the pseudo labels, a novel loss which promotes the
reconstruction of normal pixels while suppressing the reconstruction of
anomalous pixels is used. We conduct experiments on different datasets and
demonstrate the advantages of KIST over the existing reconstruction-based
methods.