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.
Variantional autoencoder with decremental information bottleneck for disentanglement
Surrey Institute for People Centred AI（萨里以人为本人工智能研究所）；University of Surrey（萨里大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Jinan（济南大学）；Mintyea Tech, Inc.（茗茶科技公司）
Disentangled Representation Learning with Transmitted Information Bottleneck
State Grid Shaanxi Electric Power Company Ltd.（国家电网陕西省电力公司）；State Grid Corporation of China（国家电网有限公司）；University of Science and Technology of China（中国科学技术大学）
Robust Training of Vector Quantized Bottleneck Models
NVIDIA Corporation（英伟达公司）；University of Wrocław（弗罗茨瓦夫大学）；Université de Toulon（土伦大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；Massachusetts Institute of Technology（麻省理工学院）；Tallinn University of Technology（塔林理工大学）；Le Mans University（勒芒大学）
