Reviews: Learning Disentangled Representations with Semi-Supervised Deep Generative Models

The authors develop a framework allowing VAE type computation on a broad class of probablistic model structures. This is motivated in particular by the idea that some lvs may have a straightforward meaning and have some labels available (e.g. which digit in MNIST/SVHN, what lighting direction in the face data), whereas others are more intangible (e.g. They propose a slightly different approach to the semi-supervised VAE of Kingma et al., by considering the (semi)supervised variables y as LVs forced to specific values for the supervised data samples. This is straightforward in the setting where q(y x) can be calculated directly, and can be handled by importance sampling if integration over z is required to calculate q(y x). Experiments are presented on MNIST, SVHN and a faces image data with variation in lighting according 38 individuals.