An Efficient Learning Framework of Sequential Variational Auto-Encoders by Sequential Filtering

Abstract

Deep sequential generative models have been used in various tasks such as time-series prediction, unseen sequence generation, and time-series anomaly detection. In this report, we focus on models so-called sequential variational auto-encoders and propose an efficient learning framework by sequential Bayes filtering. Although similar prior works provide tighter ELBOs which are lower bounds of the log marginal likelihood, several problems such as the low spread of particles in latent space remain. The proposed framework overcomes these problems by emphasizing practical use and outperforms the prior works for several datasets in predictive ability.