  We propose the factorized action variational autoencoder (FAVAE), a
state-of-the-art generative model for learning disentangled and interpretable
representations from sequential data via the information bottleneck without
supervision. The purpose of disentangled representation learning is to obtain
interpretable and transferable representations from data. We focused on the
disentangled representation of sequential data since there is a wide range of
potential applications if disentanglement representation is extended to
sequential data such as video, speech, and stock market. Sequential data are
characterized by dynamic and static factors: dynamic factors are time
dependent, and static factors are independent of time. Previous models
disentangle static and dynamic factors by explicitly modeling the priors of
latent variables to distinguish between these factors. However, these models
cannot disentangle representations between dynamic factors, such as
disentangling "picking up" and "throwing" in robotic tasks. FAVAE can
disentangle multiple dynamic factors. Since it does not require modeling
priors, it can disentangle "between" dynamic factors. We conducted experiments
to show that FAVAE can extract disentangled dynamic factors.
