Disentangled Representation Learning

arXiv:2211.11695v3 Announce Type: replace
Abstract: 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, …

abstract arxiv benefits cs.ai cs.lg data form hidden learn meaning observable process representation representation learning semantic type understanding variables variation