  We propose a novel and theoretical model, blocked and hierarchical
variational autoencoder (BHiVAE), to get better-disentangled representation. It
is well known that information theory has an excellent explanatory meaning for
the network, so we start to solve the disentanglement problem from the
perspective of information theory. BHiVAE mainly comes from the information
bottleneck theory and information maximization principle. Our main idea is that
(1) Neurons block not only one neuron node is used to represent attribute,
which can contain enough information; (2) Create a hierarchical structure with
different attributes on different layers, so that we can segment the
information within each layer to ensure that the final representation is
disentangled. Furthermore, we present supervised and unsupervised BHiVAE,
respectively, where the difference is mainly reflected in the separation of
information between different blocks. In supervised BHiVAE, we utilize the
label information as the standard to separate blocks. In unsupervised BHiVAE,
without extra information, we use the Total Correlation (TC) measure to achieve
independence, and we design a new prior distribution of the latent space to
guide the representation learning. It also exhibits excellent disentanglement
results in experiments and superior classification accuracy in representation
learning.
