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

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.

Key words: deep latent space generative models, unsupervised learning, Variational Auto-Encoders（VAEs）, image generation