Autoencoder and Its Various Variants

The concept of autoencoder was originally proposed by LeCun in 1987, early works on autoencoder were used for dimensionality reduction or feature learning. Recently, with the popularity of deep learning research, autoencoder has been brought to the forefront of generative modeling. Many variants of...

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Bibliographic Details
Main Authors: Zhai, Junhai, Zhang, Sufang, Chen, Junfen, He, Qiang
Format: Conference Proceeding
Language:English
Subjects:
autoencoder
Computational modeling
Data models
decoder
Decoding
deep learning
feature learning
Gallium nitride
Generative adversarial networks
generative model
Mathematical model
Training
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Summary:The concept of autoencoder was originally proposed by LeCun in 1987, early works on autoencoder were used for dimensionality reduction or feature learning. Recently, with the popularity of deep learning research, autoencoder has been brought to the forefront of generative modeling. Many variants of autoencoder have been proposed by different researchers and have been successfully applied in many fields, such as computer vision, speech recognition and natural language processing. In this paper, we present a comprehensive survey on autoencoder and its various variants. Furthermore, we also present the lineage of the surveyed autoencoders. This paper can provide researchers engaged in related works with very valuable help.
ISSN:2577-1655
DOI:10.1109/SMC.2018.00080