Vol. 4 No. 1 (2024): Advances in Deep Learning Techniques
Articles

Variational Autoencoders - Theory and Applications: Exploring Variational Autoencoder Models and Their Applications in Generative Modeling, Representation Learning, and Beyond

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  • Dr. Miguel Sanchez
Dr. Miguel Sanchez
Research Fellow in Evolutionary Algorithms, National University of Singapore (NUS), Singapore
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Published 27-02-2024

Keywords

  • Variational Autoencoders,
  • Generative Modeling,
  • Representation Learning,
  • Encoder,
  • Decoder,
  • Disentanglement,
  • Semi-Supervised Learning,
  • Anomaly Detection,
  • Data Augmentation
    • ...More
    Less

How to Cite

[1]
D. M. Sanchez, “Variational Autoencoders - Theory and Applications: Exploring Variational Autoencoder Models and Their Applications in Generative Modeling, Representation Learning, and Beyond”, Adv. in Deep Learning Techniques, vol. 4, no. 1, pp. 18–32, Feb. 2024.
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Abstract

Variational autoencoders (VAEs) have emerged as a powerful framework for generative modeling and representation learning in recent years. This paper provides a comprehensive overview of VAEs, starting with their theoretical foundations and then exploring their diverse applications. We begin by explaining the basic principles of VAEs, including the encoder and decoder networks, the reparameterization trick, and the variational lower bound. We then delve into various extensions and improvements to the basic VAE framework, such as conditional VAEs, hierarchical VAEs, and beta-VAEs, highlighting their respective advantages and use cases.

Moving beyond theory, we survey the wide range of applications where VAEs have been successfully employed. This includes image generation, where VAEs have been used to create realistic images in domains such as fashion, art, and medical imaging. We also discuss the use of VAEs in representation learning, showing how they can be used to disentangle underlying factors of variation in data, leading to more interpretable and controllable representations. Additionally, we explore how VAEs have been applied in semi-supervised learning, anomaly detection, and data augmentation.

Overall, this paper aims to provide a comprehensive understanding of VAEs, from their fundamental concepts to their practical applications, showcasing their versatility and potential for future research and development.

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