Autoencoders and their applications in machine learning: a survey

Autoencoders have become a hot researched topic in unsupervised learning due to their ability to learn data features and act as a dimensionality reduction method. With rapid evolution of autoencoder methods, there has yet to be a complete study that provides a full autoencoders roadmap for both stim...

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Veröffentlicht in:	The Artificial intelligence review Jg. 57; H. 2; S. 28
Hauptverfasser:	Berahmand, Kamal, Daneshfar, Fatemeh, Salehi, Elaheh Sadat, Li, Yuefeng, Xu, Yue
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 03.02.2024 Springer Springer Nature B.V
Schlagworte:	Algorithms Anomalies Artificial Intelligence Classification Computational linguistics Computer engineering Computer Science Deep learning Discriminant analysis Language processing Machine learning Machine vision Natural language interfaces Natural language processing Neural networks Polls & surveys Principal components analysis Principles Probability distribution Recommender systems Speech Taxonomy Unsupervised learning Deep learning Dimensionality reduction Reconstruction loss Autoencoder Bottleneck layer Feature extraction Autoencoder application Unsupervised learning
ISSN:	1573-7462, 0269-2821, 1573-7462
Online-Zugang:	Volltext
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