Variations in Variational Autoencoders - A Comparative Evaluation

Variational Auto-Encoders (VAEs) are deep latent space generative models which have been immensely successful in many applications such as image generation, image captioning, protein design, mu-tation prediction, and language models among others. The fundamental idea in VAEs is to learn the distribu...

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Veröffentlicht in:	IEEE access Jg. 8; S. 1
Hauptverfasser:	Wei, Ruoqi, Garcia, Cesar, El-Sayed, Ahmed, Peterson, Viyaleta, Mahmood, Ausif
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Coders data representation Deep Learning generative models Image processing latent space Mutation representation learning unsupervised learning Variational Autoencoders (VAEs)
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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