Developing semi-supervised variational autoencoder-generative adversarial network models to enhance quality prediction performance

One common serious issue of training a prediction model is that the process data significantly outnumber the quality data. Such discrepancy exists because of the time lag for obtaining quality data. This paper proposes semi-supervised variational autoencoder-generative adversarial network (S2-VAE/GA...

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Vydané v:	Chemometrics and intelligent laboratory systems Ročník 217; s. 104385
Hlavní autori:	Ooi, Sai Kit, Tanny, Dave, Chen, Junghui, Wang, Kai
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 15.10.2021
Predmet:	Generative adversarial network Latent variable model Semi-supervised variational autoencoder Soft sensors Latent variable model Soft sensors Semi-supervised variational autoencoder Generative adversarial network
ISSN:	0169-7439, 1873-3239
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