Title: Learning Disentangled Representations with Semi-Supervised Deep Generative Models,
Abstract: Variational autoencoders (VAEs) learn representations of data by jointly
training a probabilistic encoder and decoder network. Typically these models
encode all features of the data into a single variable. Here we are interested
in learning disentangled representations that encode distinct aspects of the
data into separate variables. We propose to learn such representations using
model architectures that generalise from standard VAEs, employing a general
graphical model structure in the encoder and decoder. This allows us to train
partially-specified models that make relatively strong assumptions about a
subset of interpretable variables and rely on the flexibility of neural
networks to learn representations for the remaining variables. We further
define a general objective for semi-supervised learning in this model class,
which can be approximated using an importance sampling procedure. We evaluate
our framework's ability to learn disentangled representations, both by
qualitative exploration of its generative capacity, and quantitative evaluation
of its discriminative ability on a variety of models and datasets.