Title: Disentangled VAE Representations for Multi-Aspect and Missing Data,
Abstract: Many problems in machine learning and related application areas are
fundamentally variants of conditional modeling and sampling across multi-aspect
data, either multi-view, multi-modal, or simply multi-group. For example,
sampling from the distribution of English sentences conditioned on a given
French sentence or sampling audio waveforms conditioned on a given piece of
text. Central to many of these problems is the issue of missing data: we can
observe many English, French, or German sentences individually but only
occasionally do we have data for a sentence pair. Motivated by these
applications and inspired by recent progress in variational autoencoders for
grouped data, we develop factVAE, a deep generative model capable of handling
multi-aspect data, robust to missing observations, and with a prior that
encourages disentanglement between the groups and the latent dimensions. The
effectiveness of factVAE is demonstrated on a variety of rich real-world
datasets, including motion capture poses and pictures of faces captured from
varying poses and perspectives.