计算机工程与应用2019，Vol.55Issue(3)：1-9,9.DOI:10.3778/j.issn.1002-8331.1810-0284
变分自编码器模型综述
Research Overview of Variational Auto-Encoders Models
摘要
Abstract
Variational Auto-Encoders(VAE)as one of deep latent space generative models have been immensely success on its performance in recent years, especially in image generation. VAEs models are important tools for unsupervised feature learning, which can learn a mapping from a latent encoding space to a data generative space and reconstruct the inputs to outputs. Firstly, this paper reviews the development and present research situation of the traditional variational auto-encoders and its variants, summarizes and compares the performance for all of them. Finally, the existing difficulties and challenges of VAEs are analyzed, and the possible development direction is prospected.关键词
深度隐空间生成模型/无监督学习/变分自编码器/图像生成Key words
deep latent space generative models/unsupervised learning/Variational Auto-Encoders(VAEs)/image generation分类
信息技术与安全科学引用本文复制引用
翟正利,梁振明,周炜,孙霞..变分自编码器模型综述[J].计算机工程与应用,2019,55(3):1-9,9.基金项目
国家自然科学基金(No.61673258,No.61075115,No.61403249,No.61603242). （No.61673258,No.61075115,No.61403249,No.61603242）
