A Primer on Generative Adversarial Networks

Authors

  • Vikas Thada Associate Professor, Department of Computer Science & Engineering, Amity School of Engineering & Technology, Amity University Haryana, India Author
  • Utpal Shrivastava Student, M. Tech, Department of Computer Science & Engineerin, GJUS&T, Haryana and Pursuing Ph.D. from Banasthali University Jaipur Author
  • Jyotsna Sharma Student, B.Tech, Department of Computer Science & Engineering, Amity University Haryana Author
  • Kuwar Prateek Singh Student, B.Tech, Department of Computer Science & Engineering, Amity University Haryana Author
  • Manda Ranadeep Student, B.Tech, Department of Computer Science & Engineering, Amity University Haryana, India Author

Keywords:

Deep Learning, Generative Adversarial Networks, Neural Network, Application

Abstract

Generative Adversarial Networks (GANs) is  a type of deep neural network architecture that utilizes  unsupervised machine learning to generate data. They were  presented in 2014, in a paper by Ian Goodfellow, Yoshua  Bengio, and Aaron Courville. This paper will introduce the  core components of GANs. This will take you through how  every part function and the significant ideas and innovation  behind GANs. It will likewise give a short outline of the  advantages and downsides of utilizing GANs, comparison  of architectures of various GANs and knowledge into  certain true applications.  

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References

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Published

2020-05-05

Issue

Vol. 8 No. 3 (2020): International Journal of Innovative Research in Computer Science and Technology (May) 2020

Section

Articles

How to Cite

A Primer on Generative Adversarial Networks . (2020). International Journal of Innovative Research in Computer Science & Technology, 8(3), 152–158. Retrieved from https://acspublisher.com/journals/index.php/ijircst/article/view/13287