Normalizing flows for deep anomaly detection

In this work, we consider cases with missing certain kinds of anomalies in the training dataset, while significant statistics for the normal class is available. For such scenarios, conventional supervised methods might suffer from the class imbalance, while unsupervised methods tend to ignore difficult anomalous examples. We extend the idea of the supervised classification approach for class-imbalanced datasets by exploiting normalizing flows for proper Bayesian inference of the posterior probabilities. Index Terms --Machine Learning, Neural Nets, Anomaly Detection, Imbalanced Data Set, Generate Potential Outliers, Normalizing Flow null 1 I NTRODUCTION The anomaly detection problem is one of the important tasks in the analysis of real-world data. Possible applications range from the data-quality certification [1] to finding the rare specific cases of the diseases in medicine [2].