  Recent studies give more attention to the anomaly detection (AD) methods that
can leverage a handful of labeled anomalies along with abundant unlabeled data.
These existing anomaly-informed AD methods rely on manually predefined score
target(s), e.g., prior constant or margin hyperparameter(s), to realize
discrimination in anomaly scores between normal and abnormal data. However,
such methods would be vulnerable to the existence of anomaly contamination in
the unlabeled data, and also lack adaptation to different data scenarios. In
this paper, we propose to optimize the anomaly scoring function from the view
of score distribution, thus better retaining the diversity and more
fine-grained information of input data, especially when the unlabeled data
contains anomaly noises in more practical AD scenarios. We design a novel loss
function called Overlap loss that minimizes the overlap area between the score
distributions of normal and abnormal samples, which no longer depends on prior
anomaly score targets and thus acquires adaptability to various datasets.
Overlap loss consists of Score Distribution Estimator and Overlap Area
Calculation, which are introduced to overcome challenges when estimating
arbitrary score distributions, and to ensure the boundness of training loss. As
a general loss component, Overlap loss can be effectively integrated into
multiple network architectures for constructing AD models. Extensive
experimental results indicate that Overlap loss based AD models significantly
outperform their state-of-the-art counterparts, and achieve better performance
on different types of anomalies.
