Generative Models Based on VAE and GAN for New Medical Data Synthesis

Vladislav V. Laptev, Olga M. Gerget, Nataliia A. Markova

Research output: Chapter in Book/Report/Conference proceedingChapter


The chapter deals with the construction of generative models using Variational Autoencoder (VAE) and Generative Adversarial Neural Networks to synthesize new medical data. VAE is a synthesis of two complete neural networks: an encoder E and a generator G, as well as the latent space connecting them and enabling them to carry out random transformation and interpolation. Generative Adversarial Nets (GAN) in their turn are built on the principle of interaction between a generative model (generator G) and a discriminating model (discriminator D). When creating generator G (both VAE and GAN), its architecture of a neural network based on convolutional layers, with the application of the new deep learning framework Tensorflow-addons is used. As E and D encoders, respectively, the models of transfer learning, problem domain-image feature vector are used in the work. The comparison between them is made in the chapter and the most optimal model for solving the proposed problem is selected. The chapter presents the results of the research obtained on the basis of VAE and GAN implementation.

Original languageEnglish
Title of host publicationStudies in Systems, Decision and Control
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
Publication statusPublished - 2021

Publication series

NameStudies in Systems, Decision and Control
ISSN (Print)2198-4182
ISSN (Electronic)2198-4190


  • Convolution neural network
  • Discrimination
  • Distribution
  • Generation
  • Generative adversarial nets
  • Probability
  • Training
  • Transfer learning
  • Variational autoencoder

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Control and Systems Engineering
  • Automotive Engineering
  • Social Sciences (miscellaneous)
  • Economics, Econometrics and Finance (miscellaneous)
  • Control and Optimization
  • Decision Sciences (miscellaneous)

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