Deterministic Autoencoder using Wasserstein loss for tabular data generation

Tabular data generation is a complex task due to its distinctive characteristics and inherent complexities. While Variational Autoencoders have been adapted from the computer vision domain for tabular data synthesis, their reliance on non-deterministic latent space regularization introduces limitati...

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Veröffentlicht in:	Neural networks Jg. 185; S. 107208
Hauptverfasser:	Wang, Alex X., Nguyen, Binh P.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 01.05.2025
Schlagworte:	Algorithms Autoencoder Deep Learning Deep neural networks Generative AI Humans Latent space interpolation Neural Networks, Computer Tabular data synthesis Wasserstein Autoencoder Deep neural networks Generative AI Tabular data synthesis Latent space interpolation Wasserstein Autoencoder
ISSN:	0893-6080, 1879-2782, 1879-2782
Online-Zugang:	Volltext
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