  We consider the disentanglement of the representations of the relevant
attributes of the data (content) from all other factors of variations (style)
using Variational Autoencoders. Some recent works addressed this problem by
utilizing grouped observations, where the content attributes are assumed to be
common within each group, while there is no any supervised information on the
style factors. In many cases, however, these methods fail to prevent the models
from using the style variables to encode content related features as well. This
work supplements these algorithms with a method that eliminates the content
information in the style representations. For that purpose the training
objective is augmented to minimize an appropriately defined mutual information
term in an adversarial way. Experimental results and comparisons on image
datasets show that the resulting method can efficiently separate the content
and style related attributes and generalizes to unseen data.
