  In recent years, extending variational autoencoder's framework to learn
disentangled representations has received much attention. We address this
problem by proposing a framework capable of disentangling class-related and
class-independent factors of variation in data. Our framework employs an
attention mechanism in its latent space in order to improve the process of
extracting class-related factors from data. We also deal with the multimodality
of data distribution by utilizing mixture models as learnable prior
distributions, as well as incorporating the Bhattacharyya coefficient in the
objective function to prevent highly overlapping mixtures. Our model's encoder
is further trained in a semi-supervised manner, with a small fraction of
labeled data, to improve representations' interpretability. Experiments show
that our framework disentangles class-related and class-independent factors of
variation and learns interpretable features. Moreover, we demonstrate our
model's performance with quantitative and qualitative results on various
datasets.
