  Learning rich representation from data is an important task for deep
generative models such as variational auto-encoder (VAE). However, by
extracting high-level abstractions in the bottom-up inference process, the goal
of preserving all factors of variations for top-down generation is compromised.
Motivated by the concept of "starting small", we present a strategy to
progressively learn independent hierarchical representations from high- to
low-levels of abstractions. The model starts with learning the most abstract
representation, and then progressively grow the network architecture to
introduce new representations at different levels of abstraction. We
quantitatively demonstrate the ability of the presented model to improve
disentanglement in comparison to existing works on two benchmark data sets
using three disentanglement metrics, including a new metric we proposed to
complement the previously-presented metric of mutual information gap. We
further present both qualitative and quantitative evidence on how the
progression of learning improves disentangling of hierarchical representations.
By drawing on the respective advantage of hierarchical representation learning
and progressive learning, this is to our knowledge the first attempt to improve
disentanglement by progressively growing the capacity of VAE to learn
hierarchical representations.
