Marginalized Stacked Denoising Autoencoder With Adaptive Noise Probability for Cross Domain Classification

Cross-domain classification is a challenging problem, in which, how to learn domain invariant features is critical. Recently, significant improvements to this problem have emerged with the wide application of deep learning models, which have been proposed to learn higher level and robust feature rep...

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Vydané v:	IEEE access Ročník 7; s. 143015 - 143024
Hlavní autori:	Zhang, Yuhong, Yang, Shuai, Li, Peipei, Hu, Xuegang, Wang, Hao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptation models adaptive parameter Classification Deep learning Domain adaptation Domains Feature extraction Indexes Machine learning mSDA Neural networks Noise noise probability Noise reduction Noise sensitivity Task analysis
ISSN:	2169-3536, 2169-3536
On-line prístup:	Získať plný text
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