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We propose the factorized action variational autoencoder (FAVAE), astate-of-the-art generative model for learning disentangled and interpretablerepresentations from sequential data via the information bottleneck withoutsupervision. The purpose of disentangled representation learning is to obtaininterpretable and transferable representations from data. We focused on thedisentangled representation of sequential data since there is a wide range ofpotential applications if disentanglement representation is extended tosequential data such as video, speech, and stock market. Sequential data arecharacterized by dynamic and static factors: dynamic factors are timedependent, and static factors are independent of time. Previous modelsdisentangle static and dynamic factors by explicitly modeling the priors oflatent variables to distinguish between these factors. However, these modelscannot disentangle representations between dynamic factors, such asdisentangling "picking up" and "throwing" in robotic tasks. FAVAE candisentangle multiple dynamic factors. Since it does not require modelingpriors, it can disentangle "between" dynamic factors. We conducted experimentsto show that FAVAE can extract disentangled dynamic factors.

FAVAE: Sequence Disentanglement using Information Bottleneck Principle