Vol. 1 No. 1 (2021): Advances in Deep Learning Techniques
Articles

Generative Adversarial Networks - Recent Developments: Investigating Recent Developments in Generative Adversarial Networks (GANs) for Generating Realistic Images and Other Data Types

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  • Prof. Elena Petrova,
  • Gopalakrishnan Arjunan
Prof. Elena Petrova
Professor of Artificial Intelligence, Moscow Institute of Physics and Technology, Russia
Gopalakrishnan Arjunan
AI/ML Engineer at Accenture, Bangalore, India
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Published 27-02-2021

Keywords

  • Generative Adversarial Networks,
  • GANs,
  • Image Generation,
  • Adversarial Training,
  • Synthetic Data,
  • Deep Learning,
  • Artificial Intelligence,
  • Text-to-Image Synthesis,
  • Video Generation
    • ...More
    Less

How to Cite

[1]
P. E. Petrova and G. Arjunan, “Generative Adversarial Networks - Recent Developments: Investigating Recent Developments in Generative Adversarial Networks (GANs) for Generating Realistic Images and Other Data Types”, Adv. in Deep Learning Techniques, vol. 1, no. 1, pp. 11–22, Feb. 2021.
Creative Commons License

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

Abstract

Generative Adversarial Networks (GANs) have revolutionized the field of artificial intelligence by enabling the generation of high-quality synthetic data that closely resembles real data. This paper provides a comprehensive review of recent developments in GANs, focusing on advancements in generating realistic images and other data types. We begin by exploring the fundamental concepts of GANs and their architecture, highlighting the adversarial training process. We then delve into the key advancements in GANs, including improvements in stability, diversity, and image quality. Additionally, we discuss novel applications of GANs beyond image generation, such as text-to-image synthesis and video generation. Finally, we present future research directions and challenges in the field of GANs.

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References

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