  Anomaly detection is a challenging task that frequently arises in practically
all areas of industry and science, from fraud detection and data quality
monitoring to finding rare cases of diseases and searching for new physics.
Most of the conventional approaches to anomaly detection, such as one-class SVM
and Robust Auto-Encoder, are one-class classification methods, i.e. focus on
separating normal data from the rest of the space. Such methods are based on
the assumption of separability of normal and anomalous classes, and
subsequently do not take into account any available samples of anomalies.
Nonetheless, in practical settings, some anomalous samples are often available;
however, usually in amounts far lower than required for a balanced
classification task, and the separability assumption might not always hold.
This leads to an important task -- incorporating known anomalous samples into
training procedures of anomaly detection models.
  In this work, we propose a novel model-agnostic training procedure to address
this task. We reformulate one-class classification as a binary classification
problem with normal data being distinguished from pseudo-anomalous samples. The
pseudo-anomalous samples are drawn from low-density regions of a normalizing
flow model by feeding tails of the latent distribution into the model. Such an
approach allows to easily include known anomalies into the training process of
an arbitrary classifier. We demonstrate that our approach shows comparable
performance on one-class problems, and, most importantly, achieves comparable
or superior results on tasks with variable amounts of known anomalies.
