  Visual anomaly detection is common in several applications including medical
screening and production quality check. Although a definition of the anomaly is
an unknown trend in data, in many cases some hints or samples of the anomaly
class can be given in advance. Conventional methods cannot use the available
anomaly data, and also do not have a robustness of noise. In this paper, we
propose a novel spatially-weighted reconstruction-loss-based anomaly detection
with a likelihood value from a regression model trained by all known data. The
spatial weights are calculated by a region of interest generated from employing
visualization of the regression model. We introduce some ways to combine with
various strategies to propose a state-of-the-art method. Comparing with other
methods on three different datasets, we empirically verify the proposed method
performs better than the others.
