解耦表征学习研究进展

解耦表征学习旨在对影响数据形态的关键因素进行建模，使得某一关键因素的变化仅仅引起数据在某项特征上的变化，而其他的特征不受影响，这有利于应对机器学习在模型可解释性、对象生成和操作以及零样本学习等问题上的挑战，因此解耦表征学习一直是机器学习领域的一个研究热点。从解耦表征学习的历史与动机入手，对解耦表征学习的研究现状以及应用进行归纳总结，分析了解耦表征所具有的不变性、复用性等特性，介绍了基于生成解耦表征变差因素的研究、基于流形相互作用解耦表征变差因素的研究、基于对抗性训练解耦表征变差因素的研究，以及一种变分自编码器β-VAE的研究等最新研究动态。同时，阐述了解耦表征学习的典型应用，并对未来的研究方向作出了展望。

Research advances in disentangled representation learning

The purpose of disentangled representation learning is to model the key factors that affect the form of data， so that the change of a key factor only causes the change of data on a certain feature， while the other features are not affected. It is conducive to face the challenge of machine learning in model interpretability， object generation and operation， zero-shot learning and other issues. Therefore， disentangled representation learning always be a research hotspot in the field of machine learning. Starting from the history and motives of disentangled representation learning， the research status and applications of disentangled representation learning were summarized， the invariance， reusability and other characteristics of disentangled representation learning were analyzed， and the research on the factors of variation via generative entangling， the research on the factors of variation with manifold interaction， and the research on the factors of variation using adversarial training were introduced， as well as the latest research trends such as a Variational Auto-Encoder （VAE） named β-VAE were introduced. At the same time， the typical applications of disentangled representation learning were shown， and the future research directions were prospected.