normalizing flows transform a latent distribution through an invertible
neural network for a flexible and pleasingly simple approach to generative
modelling, while preserving an exact likelihood. We propose FlowGMM, an
end-to-end approach to generative semi supervised learning with nor
Flow-based deep generative models can be used for novelty detection in time series data and outperform traditional methods like the Local Outlier Factor.