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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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| cited_by | cdi_FETCH-LOGICAL-c220t-be81b20f00d2355d0fe32dab774d24a678fcb828b7839db6844013854d1d5d713 |
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| creator | Zhai, Junhai Zhang, Sufang Chen, Junfen He, Qiang |
| description | 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. |
| doi_str_mv | 10.1109/SMC.2018.00080 |
| format | conference_proceeding |
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| identifier | EISSN: 2577-1655 |
| ispartof | 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2018, p.415-419 |
| issn | 2577-1655 |
| language | eng |
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| subjects | autoencoder Computational modeling Data models decoder Decoding deep learning feature learning Gallium nitride Generative adversarial networks generative model Mathematical model Training |
| title | Autoencoder and Its Various Variants |
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