Tabular and latent space synthetic data generation: a literature review

Tabular and latent space synthetic data generation: a literature review

Abstract The generation of synthetic data can be used for anonymization, regularization, oversampling, semi-supervised learning, self-supervised learning, and several other tasks. Such broad potential motivated the development of new algorithms, specialized in data generation for specific data forma...

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Bibliographic Details
Main Authors: Joao Fonseca, Fernando Bacao
Format: Article
Language:English
Published: SpringerOpen 2023-07-01
Series:Journal of Big Data
Subjects:
Synthetic Data
Tabular data
Data privacy
Regularization
Oversampling
Active Learning
Online Access:https://doi.org/10.1186/s40537-023-00792-7

Internet

https://doi.org/10.1186/s40537-023-00792-7

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