  Anomaly Detection is becoming increasingly popular within the experimental
physics community. At experiments such as the Large Hadron Collider, anomaly
detection is at the forefront of finding new physics beyond the Standard Model.
This paper details the implementation of a novel Machine Learning architecture,
called Flux+Mutability, which combines cutting-edge conditional generative
models with clustering algorithms. In the `flux' stage we learn the
distribution of a reference class. The `mutability' stage at inference
addresses if data significantly deviates from the reference class. We
demonstrate the validity of our approach and its connection to multiple
problems spanning from one-class classification to anomaly detection. In
particular, we apply our method to the isolation of neutral showers in an
electromagnetic calorimeter and show its performance in detecting anomalous
dijets events from standard QCD background. This approach limits assumptions on
the reference sample and remains agnostic to the complementary class of objects
of a given problem. We describe the possibility of dynamically generating a
reference population and defining selection criteria via quantile cuts.
Remarkably this flexible architecture can be deployed for a wide range of
problems, and applications like multi-class classification or data quality
control are left for further exploration.
