  Unsupervised Anomaly Detection (UAD) is a key data mining problem owing to
its wide real-world applications. Due to the complete absence of supervision
signals, UAD methods rely on implicit assumptions about anomalous patterns
(e.g., scattered/sparsely/densely clustered) to detect anomalies. However,
real-world data are complex and vary significantly across different domains. No
single assumption can describe such complexity and be valid in all scenarios.
This is also confirmed by recent research that shows no UAD method is
omnipotent. Based on above observations, instead of searching for a magic
universal winner assumption, we seek to design a general UAD Booster (UADB)
that empowers any UAD models with adaptability to different data. This is a
challenging task given the heterogeneous model structures and assumptions
adopted by existing UAD methods. To achieve this, we dive deep into the UAD
problem and find that compared to normal data, anomalies (i) lack clear
structure/pattern in feature space, thus (ii) harder to learn by model without
a suitable assumption, and finally, leads to (iii) high variance between
different learners. In light of these findings, we propose to (i) distill the
knowledge of the source UAD model to an imitation learner (booster) that holds
no data assumption, then (ii) exploit the variance between them to perform
automatic correction, and thus (iii) improve the booster over the original UAD
model. We use a neural network as the booster for its strong expressive power
as a universal approximator and ability to perform flexible post-hoc tuning.
Note that UADB is a model-agnostic framework that can enhance heterogeneous UAD
models in a unified way. Extensive experiments on over 80 tabular datasets
demonstrate the effectiveness of UADB.
