  Disentangled Representation Learning (DRL) aims to learn a model capable of
identifying and disentangling the underlying factors hidden in the observable
data in representation form. The process of separating underlying factors of
variation into variables with semantic meaning benefits in learning explainable
representations of data, which imitates the meaningful understanding process of
humans when observing an object or relation. As a general learning strategy,
DRL has demonstrated its power in improving the model explainability,
controlability, robustness, as well as generalization capacity in a wide range
of scenarios such as computer vision, natural language processing, and data
mining. In this article, we comprehensively investigate DRL from various
aspects including motivations, definitions, methodologies, evaluations,
applications, and model designs. We first present two well-recognized
definitions, i.e., Intuitive Definition and Group Theory Definition for
disentangled representation learning. We further categorize the methodologies
for DRL into four groups from the following perspectives, the model type,
representation structure, supervision signal, and independence assumption. We
also analyze principles to design different DRL models that may benefit
different tasks in practical applications. Finally, we point out challenges in
DRL as well as potential research directions deserving future investigations.
We believe this work may provide insights for promoting the DRL research in the
community.
