  Anomaly detection plays a crucial role in various real-world applications,
including healthcare and finance systems. Owing to the limited number of
anomaly labels in these complex systems, unsupervised anomaly detection methods
have attracted great attention in recent years. Two major challenges faced by
the existing unsupervised methods are: (i) distinguishing between normal and
abnormal data in the transition field, where normal and abnormal data are
highly mixed together; (ii) defining an effective metric to maximize the gap
between normal and abnormal data in a hypothesis space, which is built by a
representation learner. To that end, this work proposes a novel scoring network
with a score-guided regularization to learn and enlarge the anomaly score
disparities between normal and abnormal data. With such score-guided strategy,
the representation learner can gradually learn more informative representation
during the model training stage, especially for the samples in the transition
field. We next propose a score-guided autoencoder (SG-AE), incorporating the
scoring network into an autoencoder framework for anomaly detection, as well as
other three state-of-the-art models, to further demonstrate the effectiveness
and transferability of the design. Extensive experiments on both synthetic and
real-world datasets demonstrate the state-of-the-art performance of these
score-guided models (SGMs).
