  The problem of feature disentanglement has been explored in the literature,
for the purpose of image and video processing and text analysis.
State-of-the-art methods for disentangling feature representations rely on the
presence of many labeled samples. In this work, we present a novel method for
disentangling factors of variation in data-scarce regimes. Specifically, we
explore the application of feature disentangling for the problem of supervised
classification in a setting where few labeled samples exist, and there are no
unlabeled samples for use in unsupervised training. Instead, a similar datasets
exists which shares at least one direction of variation with the
sample-constrained datasets. We train our model end-to-end using the framework
of variational autoencoders and are able to experimentally demonstrate that
using an auxiliary dataset with similar variation factors contribute positively
to classification performance, yielding competitive results with the
state-of-the-art in unsupervised learning.
