  The ever-increasing size of modern data sets combined with the difficulty of
obtaining label information has made semi-supervised learning one of the
problems of significant practical importance in modern data analysis. We
revisit the approach to semi-supervised learning with generative models and
develop new models that allow for effective generalisation from small labelled
data sets to large unlabelled ones. Generative approaches have thus far been
either inflexible, inefficient or non-scalable. We show that deep generative
models and approximate Bayesian inference exploiting recent advances in
variational methods can be used to provide significant improvements, making
generative approaches highly competitive for semi-supervised learning.
