  Many real-world datasets can be divided into groups according to certain
salient features (e.g. grouping images by subject, grouping text by font,
etc.). Often, machine learning tasks require that these features be represented
separately from those manifesting independently of the grouping. For example,
image translation entails changing the style of an image while preserving its
content. We formalize these two kinds of attributes as two complementary
generative factors called "domain" and "content", and address the problem of
disentangling them in a fully unsupervised way. To achieve this, we propose a
principled, generalizable probabilistic model inspired by the Variational
Autoencoder. Our model exhibits state-of-the-art performance on the composite
task of generating images by combining the domain of one input with the content
of another. Distinctively, it can perform this task in a few-shot, unsupervised
manner, without being provided with explicit labelling for either domain or
content. The disentangled representations are learned through the combination
of a group-wise encoder and a novel domain-confusion loss.
