深度自动编码器的研究与展望

Research and Prospect of Deep Auto-encoders

深度学习是机器学习的一个分支,开创了神经网络发展的新纪元。作为深度学习结构的主要组成部分之一,深度自动编码器主要用于完成转换学习任务,同时在无监督学习及非线性特征提取过程中也扮演着至关重要的角色。首先介绍深度自动编码器的发展由来、基本概念及原理,然后介绍它的构建方法以及预训练和精雕的一般步骤,并对不同类型深度自动编码器进行总结,最后在深入分析深度自动编码器目前存在的问题的基础上,对其未来发展趋势进行展望。

Deep learning,which is a branch of machine learning,inaugurates new era in the development of neural network. As a key component of deep structure,the deep auto-encoder is used to fulfill a task of transforming learning and plays important role in both unsupervised learning and non-linear characters extraction. We firstly introduced the origin of deep auto-encoder as well as its basic concept and principle,secondly,the construction procedure,pre-training and fine-tune procedure of depth auto-encoders were generally introduced,meanwhile,a comprehensive summarization of different kinds of DAE was made. At last,the direction of future work was proposed based on an in-depth study of current DAE researches.